JSM 2017 Online Program

Legend:
CC = Baltimore Convention Center, H = Hilton Baltimore
* = applied session ! = JSM meeting theme

Activity Details

2 * !	Sun, 7/30/2017, 2:00 PM - 3:50 PM	CC-315
Phase Variation (Curve Registration) in Functional Data Analyzing — Invited Papers
Section on Nonparametric Statistics , Section on Statistical Learning and Data Science , Section on Statistical Computing
Organizer(s): J. S. (Steve) Marron, University of North Carolina
Chair(s): Jan Gertheiss, Clausthal University of Technology
2:05 PM	Clustering Misaligned Dependent Curves — Piercesare Secchi, Politecnico di Milano ; Konrad Abramowicz, Umea University ; Per Arnqvist, Umea University ; Sara Sj¨ostedt de Luna, Umea University ; Simone Vantini, Politecnico di Milano ; Valeria Vitelli, University of Oslo
2:30 PM	Spatial Modeling of Object Data: Analysing Dialect Sound Variations Across the UK — Shahin Tavakoli, University of Cambridge ; Davide Pigoli, University of Cambridge ; John AD Aston, University of Cambridge
2:55 PM	Data driven estimates of PDE parameters: an application to image registration — Michelle Carey, University College Dublin ; James O. Ramsay, McGill University
3:20 PM	Registration of Functional Data Using the Fisher-Rao Metric — Sebastian Kurtek, The Ohio State University ; Anuj Srivastava, Florida State University ; Wei Wu, Florida State University ; Eric Klassen, Florida State University ; J. S. (Steve) Marron, University of North Carolina
3:45 PM	Discussant: J. S. (Steve) Marron, University of North Carolina
3:45 PM	Floor Discussion

7 * !	Sun, 7/30/2017, 2:00 PM - 3:50 PM	CC-317
New Developments in Predictive Modeling of High-Dimensional Data — Invited Papers
Council of Chapters , Section on Statistical Learning and Data Science , Statistics in Business Schools Interest Group
Organizer(s): Anthony J Babinec, AB Analytics
Chair(s): Anthony J Babinec, AB Analytics
2:05 PM	ROS Regression: Integrating Regularization with Optimal Scaling for Predictive Modeling of High-Dimensional Data — Jacqueline J Meulman, Leiden University
2:30 PM	Comparing Correlated Component Regression with Lasso for Variable Selection in Logistic Regression with High-Dimensional Data — Jay Magidson, Statistical innovations
2:55 PM	New Problem Settings for Predictive Modeling of High-Dimensional Data — Vladimir Cherkassky, University of Minnesota
3:20 PM	Discussant: Yunzhang Zhu, Ohio State University
3:45 PM	Floor Discussion

10 * !	Sun, 7/30/2017, 2:00 PM - 3:50 PM	CC-308
General-Purpose Fast Accurate Bayesian Computation at Big-Data Scale — Invited Papers
International Society for Bayesian Analysis (ISBA) , Section on Bayesian Statistical Science , Section on Statistical Learning and Data Science
Organizer(s): David Draper, University of California, Santa Cruz
Chair(s): David Draper, University of California, Santa Cruz
2:05 PM	Scalable Bayesian Inference with Hamiltonian Monte Carlo — Michael Betancourt, University of Warwick
2:25 PM	Coresets for Scalable Bayesian Logistic Regression — Tamara Broderick, MIT
2:45 PM	Communication-Efficient Likelihood Approximation — Michael Jordan, UC Berkeley ; Jason Lee ; Yun Yang, Florida State University
3:05 PM	ABC Random Forests — Jean-Michel Marin, University of Montpellier
3:25 PM	Bayesian Inference in Parallel and Distributed Environments: The Hardware/Software Approach to Scalable Computation — Alexander Terenin, University of California, Santa Cruz ; David Draper, University of California, Santa Cruz
3:45 PM	Floor Discussion

16 !	Sun, 7/30/2017, 2:00 PM - 3:50 PM	CC-340
Recent Advances and Challenges in High-Dimensional Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science , IMS , Section on Nonparametric Statistics
Organizer(s): Pierre Bellec, Rutgers University
Chair(s): Dan Yang, Rutgers University
2:05 PM	On the Asymptotic Performance of Bridge Estimators — Arian Maleki, Columbia Univ ; Haolei Weng, Columbia University ; Shuaiwen Wang, Columbia University
2:25 PM	A General Framework for Uncovering Dependence Networks — Johannes Lederer, University of Washington
2:45 PM	The Generalized Lasso Problem and Uniqueness — Alnur Ali ; Ryan Tibshirani, Carnegie Mellon University
3:05 PM	Distributed Statistical Estimation and Rates of Convergence in Normal Approximation — Stanislav Minsker, University of Southern California
3:25 PM	Slope Meets Lasso in Sparse Linear Regression — Pierre Bellec, Rutgers University
3:45 PM	Floor Discussion

40	Sun, 7/30/2017, 2:00 PM - 3:50 PM	CC-345
Statistical Learning: Theory and Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Zhengling Qi, University of North Carolina, Chapel Hill
2:05 PM	Matched Learning (M-Learning) for Estimating Optimal Individualized Treatment Rules — Peng Wu, Columbia University ; Yuanjia Wang , Columbia University
2:20 PM	Matrix Completion with Covariate Information — Xiaojun Mao, Iowa State University ; Song Xi Chen, Iowa State University ; Raymond Wong, Iowa State University
2:35 PM	Constant-Gain Stochastic Gradient Algorithm in Nonstationary Environment — Jingyi Zhu, Johns Hopkins University Applied Mathematics and Statistics ; James C Spall, Applied Physics Laboratory
2:50 PM	Statistical Distances and Two-Sample Multivariate Goodness-of-Fit Tests — Yang Chen, University at buffalo, Department of Biostatistics ; Marianthi Markatou, University at buffalo, Department of Biostatistics ; Georgios Afendras, University at buffalo, Department of Biostatistics ; Bruce George Lindsay, The Pennsylvania State University, Department of Statistics
3:05 PM	A Smoothed Monotonic Regression via L2 Regularization — Oleg Sysoev, Linkoping University ; Oleg Burdakov, Linkoping University
3:20 PM	Estimation of Henze-Penrose Mutual Information via Minimal Spanning Trees — Salimeh Yasaei Sekeh, EECS, University of Michigan ; Alfred Hero, EECS, University of Michigan
3:35 PM	Optimal Robust Estimation of the Heavy-Tail Exponent — Shrijita Bhattacharya, University of Michigan ; Stilian Stoev, University of Michigan

41	Sun, 7/30/2017, 2:00 PM - 3:50 PM	CC-346
Statistical Analysis of Networks — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Boang Liu, University of Michigan
2:05 PM	Randomziation and Permutation Tests of Network Structure — Mark M. Fredrickson, University of Illinois at Urbana-Champaign
2:20 PM	The Use of Artificial Neural Network in Time Series Forecasting — Taysseer Sharaf, University of Michigan- Dearborn
2:35 PM	Composite Likelihood Estimation for Random-Effect Network Models — Yanjun He, University of Washington ; Peter Hoff, Duke University
2:50 PM	Network Inference Using Multi-Hub Models — Jirui Wang, George Mason Univ ; Yunpeng Zhao, George Mason University
3:05 PM	Bayesian Regression for Undirected Graphs — Wing Yan Yuen, The Chinese University of Hong Kong ; Yingying Wei, The Chinese University of Hong Kong
3:20 PM	A Hierarchical Model for Network Data in a Latent Hyperbolic Space — Anna Smith ; Catherine Calder, The Ohio State University
3:35 PM	Data integration using identification and analysis of a statistical causal network to hypothesize stable pathways through different biological levels — Azam Yazdani, The University of Texas School of Public Health, Houston ; Akram Yazdani, Icahn School of Medicine at Mount Sinai

50 !	Sun, 7/30/2017, 4:00 PM - 5:50 PM	CC-327
Machine Learning and Statistical Inference: Building Breiman's Bridge — Invited Papers
Section on Statistical Learning and Data Science , Section on Nonparametric Statistics , Section on Statistical Computing
Organizer(s): Lucas K Mentch, University of Pittsburgh
Chair(s): Giles J Hooker, Cornell University
4:05 PM	Walk on Random Forests — Erwan Scornet, Ecole Polytechnique ; Gérard Biau, University Paris 6 ; Jean-Philippe Vert, Mines Institut Curie
4:30 PM	Central Limit Theorems and Hypothesis Tests for Random Forests — Lucas K Mentch, University of Pittsburgh ; Giles J Hooker, Cornell University
4:55 PM	Detecting Local Sparsity and High-Order Interactions with Iterative Random Forests — Sumanta Basu, Cornell University ; James Bentley Brown, Lawrence Berkeley National Laboratory ; Bin Yu, University of California, Berkeley
5:20 PM	Urban Analytics Using Tree Models — Shane Jensen, The Wharton School, University of Pennsylvania
5:45 PM	Floor Discussion

73	Sun, 7/30/2017, 4:00 PM - 5:50 PM	CC-330
Nonparametric Statistics in High-Dimensional Settings — Contributed Papers
Section on Nonparametric Statistics , Section on Statistical Learning and Data Science
Chair(s): Zsolt Talata, University of Kansas
4:05 PM	Nonparametric non-convex penalized regression spline and application in ECG recording — Yao Chen, Purdue University ; Xiao Wang, Purdue University ; Mohammad Adibuzzaman, Purdue University ; Yonghan Jung, Purdue University
4:20 PM	Ultra-high-dimensional Additive Partially Linear Models — Xinyi Li, Iowa State University ; Li Wang, Iowa State University ; Dan Nettleton, Iowa State University
4:35 PM	Additive Partially Linear Models for Massive Heterogeneous Data — Binhuan Wang, New York University School of Medicine ; Yixin Fang, New Jersey Institute of Technology ; Heng Lian, City University of Hong Kong ; Hua Liang , George Washington University
4:50 PM	Finding needles in a haystack? Covariate Information for Feature Screening in Ultrahigh-Dimensional Data — Debmalya Nandy, Penn State University ; Francesca Chiaromonte, Penn State University ; Runze Li, The Pennsylvania State University
5:05 PM	Sparse Covariance Estimation via Concentration Inequalities — Adam Kashlak, Univ of Cambridge ; Linglong Kong, University of Alberta
5:20 PM	Finite Sample Breakdown Point for Sliced Inverse Regression — Ulrike Genschel, Iowa State University
5:35 PM	Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Nonlinear Regression, Estimation, and Inference — Chih-Li Sung, Georgia Institute of Technology ; Wenjia Wang, Georgia Institute of Technology ; Matthew Plumlee, University of Michigan ; Benjamin Haaland, Georgia Institute of Technology

80	Sun, 7/30/2017, 4:00 PM - 5:50 PM	CC-347
Inference Methods for High-Dimensional and Complex Data — Contributed Papers
Section on Statistics in Imaging , Section on Statistical Learning and Data Science , Statistics Without Borders
Chair(s): William Lamberti, George Mason Univ
4:05 PM	Local Nearest Neighbour Classification with Applications to Semi-Supervised Learning — Timothy I. Cannings, Universtiy of Southern California ; Thomas Berrett, University of Cambridge ; Richard J. Samworth, Statistical Laboratory, University of Cambridge
4:20 PM	Adaptive Large-Scale Testing under Heterogeneity Sparsity — Xiang Lyu, Purdue University ; Guang Cheng, Purdue
4:35 PM	Valid Stepwise Regression — Kory Johnson, University of Vienna ; Dean Foster, Amazon ; Robert Stine, University of Pennsylvania
4:50 PM	Bounded-Width Confidence Interval for Gini Index Under Complex Survey — Francis Bilson Darku, University of Texas at Dallas ; Bhargab Chattopadhyay, Indian Institute of Information Technology - Vadodara
5:05 PM	Testing Independence Between Two Categorical Variables — Kenneth Liu, Merck & Co.
5:20 PM	Floor Discussion

81	Sun, 7/30/2017, 4:00 PM - 5:50 PM	CC-322
Graphical Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jianyu Liu, University of North Carolina at Chapel Hill
4:05 PM	A Fused Latent and Graphical Model for Multivariate Binary Data — Yunxiao Chen, Emory University ; Xiaoou Li, University of Minnesota Twin Cities ; Jingcheng Liu, Columbia University ; Zhiliang Ying, Columbia University
4:20 PM	Bayesian Estimation of Multiple Gaussian Graphical Models with Unknown Class — Jiali Lin, Virginia Tech ; Inyoung Kim, Virginia Tech
4:35 PM	Sparse Precision Matrix Estimation with Bayesian Regularization — Lingrui Gan, University of Illinois At Urbana and Champaign ; Naveen N Narisetty, University of Illinois at Urbana-Champaign ; Feng Liang, University of Illinois at Urbana Champaign
4:50 PM	Moralization and Interventions for DAG Model Learning — Gunwoong Park, University of Michigan
5:05 PM	Multivariate Gaussian Network Structure Learning — Xingqi Du, North Carolina State University ; Subhashis Ghoshal, North Carolina State University
5:20 PM	Inference in Gaussian DAGs with Known Partial Ordering — Syed Rahman, University of Florida
5:35 PM	Neighborhood Selection for Gaussian Graphical Model with Vecotr-Valued Nodes — Yanzhen Deng

83	Sun, 7/30/2017, 8:30 PM - 10:30 PM	CC-Halls A&B
Your Invited Poster Evening Entertainment: No Longer Board — Invited Poster Presentations
Astrostatistics Special Interest Group , Biometrics Section , Biopharmaceutical Section , Business and Economic Statistics Section , ENAR , Government Statistics Section , IMS , International Society for Bayesian Analysis (ISBA) , Section for Statistical Programmers and Analysts , Section on Statistical Consulting , Section on Statistical Education , Section on Statistical Learning and Data Science , Section on Statistics and the Environment , Social Statistics Section , Survey Research Methods Section , Section on Statistics in Genomics and Genetics
Chair(s): Jessi Cisewski, Yale University
1:	Overview of SAMSI Program on Statistical, Mathematical and Computational Methods for Astronomy (ASTRO) — Gutti Jogesh Babu, The Pennsylvania State University ; David Jones, SAMSI / Duke
2:	A Multi-Resolution 3D Map of the Intergalactic Medium via the Lyman-Alpha Forest — Collin Eubanks, Carnegie Mellon University ; Jessi Cisewski, Yale University ; Rupert Croft, Carnegie Mellon University ; Doug Nychka, National Center for Atmospheric Research ; Larry Wasserman, Carnegie Mellon
3:	Testing Bayesian Galactic Mass Estimates Using Outputs from Hydrodynamical Simulations — Gwendolyn Eadie, McMaster University ; Benjamin Keller, McMaster University ; William Harris, McMaster University ; Aaron Springford, Queen's University
4:	Quantifying Discovery in Astro/Particle Physics: Frequentist and Bayesian Perspectives — David Van Dyk, Imperial College London ; Sara Algeri, Imperial College London ; Jan Conrad, University of Stockholm
5:	Computer Model Calibration to Enable Disaggregation of Large Parameter Spaces, with Application to Mars Rover Data — David Craig Stenning, SAMSI/Duke University ; Working Group 1 ASTRO Program, SAMSI
6:	The Association Between Copy Number Aberration, DNA Methylation, and Gene Expression — Wei Sun, Fred Hutchinson Cancer Research Center
7:	Rerandomization: a Flexible Framework for Experimental Design — Kari Lock Morgan, Penn State University
8:	IMs for IVs: An Inferential Model Approach to Instrumental Variable Regression — Nicholas Aaron Syring, NCSU ; Ryan Martin, NCSU
9:	Detecting Differential Gene Expression by Single-Cell RNA Sequencing — Mingyao R Li, University of Pennsylvania ; Cheng Jia, University of Pennsylvania ; Nancy Ruonan Zhang, Wharton School , University of Pennsylvania
10:	Statistical Science and Policy at the EPA — Elizabeth Mannshardt, US Environmental Protection Agency
11:	Approximate Message Passing Algorithms for High-Dimensional Regression — Cynthia Rush, Columbia University
12:	Generalized Fiducial Inference for High-Dimensional Data — Jan Hannig, University of North Carolina at Chapel Hill ; Jonathan P Williams, University of North Carolina at Chapel Hill
13:	The Combination of Confirmatory and Contradictory Statistical Evidence at Low Resolution — Ruobin Gong, Harvard University ; Xiao-Li Meng, Harvard University
14:	Approximate Confidence Distribution Computing: An Effective Likelihood-Free Method with Statistical Guarantees — Suzanne Thornton, Rutgers University ; Minge Xie, Rutgers University
15:	R Package TDA for Statistical Inference on Topological Data Analysis — Jisu Kim, Carnegie Mellon University
16:	Teaching a Large, Project-Based Statistical Consulting Class — Emily Griffith, NC State University
17:	Transforming Undergraduate Statistics Education Through Experiential Learning: It's Essential! — Tracy Morris, University of Central Oklahoma ; Cynthia Murray, University of Central Oklahoma ; Tyler Cook, University of Central Oklahoma
18:	The Geometry of Synchronization Problems and Learning Group Actions — Tingran Gao, Duke University ; Jacek Brodzki, University of Southampton ; Sayan Mukherjee, Duke University
19:	Sufficient Markov Decision Processes with Alternating Deep Neural Networks — Longshaokan Wang, North Carolina State University ; Eric Laber, North Carolina State University ; Katie Witkiewitz, University of New Mexico
20:	Optimal Dynamic Treatment Regimes Using Decision Lists — Yichi Zhang, Harvard University ; Eric Laber, North Carolina State University ; Anastasios (Butch) Tsiatis, North Carolina State University ; Marie Davidian, North Carolina State University
21:	Predicting Phenotypes from Microarrays Using Amplified, Initially Marginal, Eigenvector Regression — Lei Ding, Indiana University ; Daniel J. McDonald, Indiana University
22:	Computer Vision Meets Television — Taylor Arnold, University of Richmond ; Lauren Tilton, University of Richmond
23:	Generalized Fiducial Inference for Nonparametric Function Estimation — Randy C.S. Lai, University of Maine
24:	A Phylogenetic Transform Enhances Analysis of Compositional Microbiota Data — Justin David Silverman, Duke University ; Sayan Mukherjee, Duke University ; Lawrence A David, Duke University
25:	Bayesian Multispecies Ecological Models for Paleoclimate Reconstruction Using Inverse Prediction — John Tipton, Colorado State University ; Mevin Hooten, Colorado State University
26:	Fast Maximum Likelihood Inference for Spatial Generalized Linear Mixed Models — Yawen Guan, Penn State University ; Murali Haran, Pennsylvania State University
27:	Fair Prediction with Disparate Impact: a Study of Bias in Recidivism Prediction Instruments — Alexandra Chouldechova, Carnegie Mellon University
28:	I Ran a Nonresponse Follow-Up Survey; Now What Do I Do? — Phillip Kott, RTI

92	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-341
Computational Challenges in Statistics — Invited Papers
IMS , Section on Statistical Learning and Data Science
Organizer(s): Sahand Negahban, Yale University
Chair(s): Sahand Negahban, Yale University
8:35 AM	Pairwise Comparison Models for High-Dimensional Ranking: Some Statistical and Computational Trade-Offs — Sivaraman Balakrishnan, Department of Statistics, CMU ; Nihar B Shah, Univ of California - Berkeley ; Martin J. Wainwright, EECS and Statistics, University of California, Berkeley
9:00 AM	On the Kiefer-Wolfowitz MLE for Gaussian Mixtures — Adityanand Guntuboyina, UC Berkeley
9:25 AM	Modeling Disease Propagation in Networks: Source-Finding and Influence Maximization — Po-Ling Loh, UW-Madison
9:50 AM	Guaranteed Tensor PCA with Optimality in Statistics and Computation — Anru Zhang, University of Wisconsin-Madison ; Dong Xia, University of Wisconsin-Madison
10:15 AM	Floor Discussion

96 !	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-344
New Statistical Methods with Distributed and Parallel Algorithms — Invited Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association , Section on Statistical Computing
Organizer(s): Xiaoming Huo, Georgia Institute of Technology
Chair(s): Manjari Narayan, Stanford University
8:35 AM	Embracing Blessing of Massive Scale in Big Data — Guang Cheng, Purdue ; Stanislav Volgushev, Univ of Toronto ; Shih-Kang Chao, Purdue
9:05 AM	High-Dimensional Non-Standard Regression — Hui Zou, University of Minnesota
9:35 AM	Discussant: Xiaoming Huo, Georgia Institute of Technology
10:05 AM	Floor Discussion

98 !	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-336
Statistical Learning for Dependent Data — Invited Papers
Business and Economic Statistics Section , Section on Statistical Learning and Data Science , Section on Nonparametric Statistics
Organizer(s): Xianyang Zhang, Texas A&M University
Chair(s): Mohsen Pourahmadi, Texas A&M University
8:35 AM	Group Orthogonal Greedy Algorithm for Change-Point Estimation of Multivariate Time Series — Chun Yip Yau, Chinese University of Hong Kong
9:00 AM	Autoregressive Model for Matrix Valued Time Series — Rong Chen, Rutgers University ; Dan Yang, Rutgers University ; Han Xiao, Rutgers University
9:25 AM	Estimation of Sparse Vector Autoregressive Moving Averages — David S Matteson, Cornell University ; Ines Wilms, KU Leuven ; Jacob Bien, Cornell University
9:50 AM	Time Series Model Fitting and Regularization Methods — Soumendra N Lahiri, North Carolina State University
10:15 AM	Floor Discussion

101	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-310
Foundation for Big Data Analysis — Invited Papers
IMS , Section on Statistical Learning and Data Science , Section on Nonparametric Statistics , Section for Statistical Programmers and Analysts
Organizer(s): Jianqing Fan, Princeton University
Chair(s): Jianqing Fan, Princeton University
8:35 AM	Inference for Large Networks — Peter Bickel, UC Berkeley ; purna sarkar, u. of texas ; Soumendu Mukherjee, UC Berkeley
9:00 AM	Inference for Big Data — Larry Wasserman, Carnegie Mellon
9:25 AM	Eigenvalues and Variance Components — Iain M Johnstone, Stanford Universty
9:50 AM	Error Variance Estimation in Ultra-High Dimensional Regression Models — Runze Li, The Pennsylvania State University ; Zhao Chen, The Pennsylvania State University ; Jianqing Fan, Princeton University
10:15 AM	Floor Discussion

108 * !	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-337
Junior Research in Bayesian Modeling for High-Dimensional Data — Topic Contributed Papers
International Society for Bayesian Analysis (ISBA) , Section on Statistics in Genomics and Genetics , Section on Statistical Learning and Data Science
Organizer(s): Valerie Poynor, California State University, Fullerton
Chair(s): James Johndrow, Stanford University
8:35 AM	Uncertainty Quantification for Network Regression — Frank W Marrs, Colorado State University ; Bailey Fosdick, Colorado State University ; Tyler McCormick, University of Washington
8:55 AM	Scalable Approximations of Marginal Posteriors in Variable Selection — Willem Van Den Boom, Duke University ; Galen Reeves, Duke University ; David Dunson, Duke University
9:15 AM	Bayesian Multi-Study Factor Analysis in High-Dimensional Biological Data — Roberta De Vito, Princeton
9:35 AM	Bayesian Multiscale Spatial Models for Big Data — Raj Guahniyogi, UCSC
9:55 AM	Cancer Phylogenies and Nonparametric Clustering — Jeffrey Miller, Harvard School of Public Health ; Scott Carter, Dana-Farber Cancer Institute
10:15 AM	Floor Discussion

109 * !	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-329
Learning from External Covariates in High-Dimensional Genomic Data Analysis — Topic Contributed Papers
Section on Statistics in Genomics and Genetics , Section on Statistical Learning and Data Science
Organizer(s): Simina Boca, Georgetown University Medical Center
Chair(s): Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health
8:35 AM	A Regression Framework for the Proportion of True Null Hypotheses — Simina Boca, Georgetown University Medical Center ; Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health
8:55 AM	Covariate-Powered Weighted Multiple Testing with False Discovery Rate Control — Huber Wolfgang, EMBL ; Nikos Ignatiadis, Stanford University
9:15 AM	Data-Driven Penalization for High-Dimensional Regression and Classification Using External Covariates — Britta Velten, EMBL Heidelberg ; Wolfgang Huber, EMBL Heidelberg
9:35 AM	Empirical Bayes Learning from Co-Data in High-Dimensional Prediction Settings — Mark Van De Wiel, VU University medical center
9:55 AM	Floor Discussion

111 * !	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-312
New Dimension Reduction and Statistical Learning Methods for Biomedical Data — Topic Contributed Papers
Biometrics Section , Section on Statistical Learning and Data Science
Organizer(s): Shanshan Ding, University of Delaware
Chair(s): Shanshan Ding, University of Delaware
8:35 AM	Longitudinally Measured Predictors: Approaches for Sufficient Dimension Reduction — Efstathia Bura, TU Wien ; Liliana Forzani, Universidad Nacional del Litoral ; Ruth Pfeiffer , National Cancer Institute, NIH, HHS
8:55 AM	On Reject and Refine Options in Multicategory Classification — Chong Zhang, Seattle, Washington ; Wenbo Wang, Binghamton University ; Xingye Qiao, Binghamton University
9:15 AM	Structured Mixture of linear mappings in high dimension — Florence Forbes, French Institute for Research in Computer Science and Automation (INRIA) ; Chun-Chen Tu, Universilty of Michigan ; Naisyin Wang, Universilty of Michigan ; Benjamin Lemasson, INSERM, Univ. Grenoble Alpes
9:35 AM	Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension — Wei Qian, Rochester Institute of Technology ; Shanshan Ding, University of Delaware ; Dennis Cook, University of Minnesota
9:55 AM	Floor Discussion

120	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-325
SPEED: Variable Selection and Networks — Contributed Speed
Section on Statistical Learning and Data Science , Section on Statistics in Genomics and Genetics
Chair(s): Zhengwu Zhang, SAMSI
8:40 AM	A Robust Model-Free Feature Screening Method for Ultrahigh-Dimensional Data — Jingnan Xue, Texas A&M University ; Faming Liang, University of Florida
8:45 AM	Dynamic Latent Factor Modeling of UN Voting Networks — Bomin Kim, Pennsylvania State University ; Xiaoyue Niu, Penn State University ; David Hunter, The Pennsylvania State University ; Xun Cao, The Pennsylvania State University
8:50 AM	Variable Selection via Phony Variables — Wenhao Hu, North Carolina State University ; Eric Laber, North Carolina State University ; Leonard Stefanski, North Carolina State University
8:55 AM	Impact of Divergence of Training and Testing Sets on Predictive Risk and Measure of Model Complexity — Jieyi Jiang, Ohio State University ; Yoonkyung Lee, The Ohio State University ; Steven MacEachern, The Ohio State University
9:00 AM	Bayesian Adjustment for Confounding When Estimating Average Causal Effects for Time-To-Event Outcomes — Li Xu ; Arnold Stromberg, Department of Statistics, University of Kentucky ; Chi Wang, Cancer Biostatistics, University of Kentucky
9:05 AM	Efficient causal structure learning in high dimensions — Arjun Sondhi, University of Washington ; Ali Shojaie, University of Washington
9:10 AM	Assessing Variable Importance Nonparametrically Using Machine Learning Techniques — Brian Williamson, University of Washington ; Marco Carone, University of Washington Department of Biostatistics ; Noah Simon, University of Washington ; Peter Gilbert, Fred Hutchinson Cancer Research Center
9:15 AM	Varying-Coefficient Models for Dynamic Networks — Jihui Lee, Columbia University ; Gen Li, Columbia University ; James D. Wilson, University of San Francisco
9:20 AM	Parsimonious and Efficient Construction of Composite Likelihood Equations by L1-Penalization — Zhendong Huang
9:30 AM	Cross Validation for Penalized M-Estimation with a Case-Weight Adjusted Solution Path — Shanshan Tu, The Ohio State University ; Yunzhang Zhu, Ohio State University ; Yoonkyung Lee, The Ohio State University
9:35 AM	Finite Sample Estimation in General Vector Autoregressive Processes — Mohamad Kazem Shirani Faradonbeh, University of Michigan ; Ambuj Tewari, University of Michigan ; George Michailidis, University of Florida
9:40 AM	A Regularization Method for Detecting Differential Item Functioning Under the Framework of Generalized Linear Models — Jing Jiang, Boston College ; Zhushan Li, Boston College
9:45 AM	Collaborative Spectral Clustering in Attributed Networks — Xiaodong Jiang ; Pengsheng Ji, University of Georgia
9:50 AM	Structural Discovery in Temporal Networks — Shaojun Zhang, University of Florida ; George Michailidis, University of Florida
9:55 AM	Variable Selection for High-Dimensional Data via Generalized Penalty — Mingwei Sun, University of Alabama ; Pu Patrick Wang, University of Alabama
10:00 AM	Neighborhood Selection with Application to Social Networks — Nana Wang ; Wolfgang Polonik, University of California, Davis
10:05 AM	Detection of Treatment Effect After Variable Selection Under Model Misspecification — Jingshen Wang, University of Michigan ; Xuming He, University of Michigan
10:10 AM	Fostering Undergraduate Data Science — Mark Ward, Purdue University ; Fulya Gokalp Yavuz, Purdue University and Yildiz Technical University
10:15 AM	Model-Based Community Detection for Networks with Node Covariates — Boang Liu, University of Michigan ; Ji Zhu, University of Michigan

121	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-326
SPEED: Environmental Statistics — Contributed Speed
Section on Statistics and the Environment , Section on Statistical Learning and Data Science , Statistics Without Borders
Chair(s): David Craig Stenning, SAMSI/Duke University
8:35 AM	'on-The-Fly' Goodness-of-Fit and Outlier Testing for Left-Censored Data — Kirk Cameron, MacStat Consulting, Ltd.
8:40 AM	Spatially Varying Autoregressive Models for Prediction of New HIV Diagnoses — Lyndsay Shand ; Bo Li, University of Illinois
8:45 AM	Modeling Spatial Extremes Using Positive Stable Mixtures — Gregory Bopp
8:50 AM	Computer Model Calibration via the Ensemble Kalman Filter — Seiyon Lee, Pennsylvania State University ; Murali Haran, Pennsylvania State University ; Klaus Keller, Pennsylvania State University
8:55 AM	Unsupervised Self-Normalized Change-Point Testing for Time Series — Liliya Lavitas, Boston University ; Ting Zhang, Boston University
9:00 AM	Migratory Bird Surveys and the Modeling of Seabird Populations — Robert Fowler
9:05 AM	Extreme Value Based Methods for Modeling Elk Dispersal — Dhanushi Wijeyakulasuriya, Pennsylvania State University ; Ephraim M Hanks, The Pennsylvania State University ; Benjamin A Shaby, Penn State University
9:10 AM	Single index model for inhomogeneous spatial point processes — Ji Meng Loh ; Yixin Fang, New Jersey Institute of Technology
9:20 AM	Copula-Based Estimation for Markov Models with Detection Limits — Fuyuan Li, George Washington University
9:25 AM	Rank-Based Estimation for Generalized Additive Models with an Application to Fisheries Data — Hannah Correia, Auburn University ; Asheber Abebe, Auburn University
9:30 AM	Estimates of Extreme Rainfall Frequency from Spatially Dense Observations — Lynne Seymour, University of Georgia ; Kyle Mattingly, University of Georgia ; Paul Miller, University of Georgia
9:35 AM	Using generalized linear models to refine management of marten trap lines — Alyssa Crawford, Alaska Department of Fish and Game
9:40 AM	Green Power Statistics: Local Wind Speed Modeling as Basis for Wind Turbine Performance Prediction — Marina Nechayeva, LaGuardia Community College ; Malgorzata Marciniak, LaGuardia Community College ; Vladimir Przhebelskiy , LaGuardia Community College ; Michael Wiley, LaGuardia Community College ; Paul DeVries, LaGuardia Community College
9:50 AM	Constrained Functional Clustering of Arctic Sea Ice Extent Data — Tan Tran, Montana State University ; Christopher Barbour, Montana State University ; Mark Greenwood, Montana State University
9:55 PM	Floor Discussion

131	Mon, 7/31/2017, 8:30 AM - 10:20 AM	CC-345
Predictive Modeling in Data Science — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Anna Smith
8:35 AM	An Overview of Existing and a Novel Approaches to Multi-Label Classification — Hyukjun Gweon ; Matthias Schonlau, University of Waterloo ; Stefan Steiner, University of Waterloo
8:50 AM	Empirical Bayes Analysis of Relevance Vector Machines — Anand Dixit, Iowa State University ; Vivekananda Roy, Iowa State University
9:05 AM	Bin-Weighted Ensemble Classifiers — Karsten Maurer, Miami University ; Walter Bennette, Air Force Research Lab
9:20 AM	Delayed Greedy Algorithm for Classification and Regression Trees — Kyle Caudle, South Dakota School of Mines and Technology ; Larry Pyeatt, South Dakota School of Mines and Technology ; Patrick Fleming, South Dakota School of Mines and Technology
9:35 AM	SVM-CART for Disease Classification — Evan Reynolds, University of Michigan ; Mousumi Banerjee, University of Michigan ; Brian Callaghan, University of Michigan
9:50 AM	Variable Selection Using Intersection and Average of Random Forests — Faraz Niyaghi, Oregon State University ; Sharmodeep Bhattacharyya, Oregon State University ; Sarah C Emerson, Oregon State University
10:05 AM	Selected Model Averaging in High-Dimensional Linear Regression — Craig Rolling, Saint Louis University ; Yongli Zhang, University of Oregon

CE_17C	Mon, 7/31/2017, 8:30 AM - 5:00 PM	H-Holiday Ballroom 6
Successful Data Mining in Practice (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Statistical Learning and Data Science
Instructor(s): Richard De Veaux, Williams College
8:30 AM	Successful Data Mining in Practice (ADDED FEE) — Richard De Veaux, Williams College

136 * !	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-308
Toward a Learning-Health System: Methods and Strategies for Data-Driven Health Care — Invited Papers
Health Policy Statistics Section , Society for Medical Decision Making , Biometrics Section , Section on Statistical Learning and Data Science
Organizer(s): Rebecca Yates Coley, Kaiser Permanente Washington Health Research Institute
Chair(s): Zhenke Wu, University of Michigan
10:35 AM	Improving Dynamic Predictions from Joint Models of Longitudinal and Survival Data Using Time-Varying Effects — Dimitris Rizopoulos, Erasmus University Medical Center
11:00 AM	Sending Analysis to the Data: Optimizing Information Exchange in the Learning Health Care System — Darren Toh, Harvard Medical School and Harvard Pilgrim Health Care Institute
11:25 AM	Learning Health Care Systems in Context: The Information, Economic, and Ethical Considerations Revolutionizing Medicine — Mary Helen Cooke, The Johns Hopkins Health System
11:50 AM	Statisticians Leading the Way: Advocating for Learning Health Systems and Collaborating Effectively with Clinical Stakeholders — Rebecca Yates Coley, Kaiser Permanente Washington Health Research Institute ; Scott Zeger, Johns Hopkins University
12:15 PM	Floor Discussion

141 * !	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-342
Statistical Analysis of Cyber-Security Data — Invited Papers
Royal Statistical Society , Section on Statistics in Defense and National Security , Section on Statistical Learning and Data Science
Organizer(s): Marina Evangelou, Imperial College London
Chair(s): Niall Adams, Imperial College
10:35 AM	Big Network Modeling and Anomaly Detection for Cyber-Security Applications — Patrick Rubin-Delanchy, University of Oxford
11:00 AM	Adaptive Threshold Selection for Trust-Based Intrusion Detection Systems — Younghun Chae, University of Rhode Island ; Natallia V Katenka, University of Rhode Island ; Lisa DiPippo, University of Rhode Island
11:25 AM	Modeling Computer Network Data Using Markov Modulated Poisson Processes — Mark Briers, Alan Turing Institute
11:50 AM	Modelling User Behavior using Endpoint Host Data — Melissa Turcotte, Los Alamos National Laboratory
12:15 PM	Floor Discussion

150 *	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-311
Recent Advances for Modeling Neuroimaging Data — Topic Contributed Papers
Section on Statistics in Imaging , International Chinese Statistical Association , Section on Statistical Learning and Data Science
Organizer(s): Fengqing Zhang, Drexel University
Chair(s): Jiangtao Gou, Hunter College of CUNY
10:35 AM	Discovering Linked Dimensions of Psychopathology and Dysconnectivity in High-Dimensional Brain Networks — Theodore Satterthwaite, Univ of Pennsylvania ; Cedric H Xia, UPenn ; Rastko Ciric, University of Pennsylvania ; Zongming Ma, University of Pennsylvania ; Russell Taki Shionhara, UPenn ; Richard Betzel, UPenn ; Monica E Calkins, UPenn ; Phillip A Cook, UPenn ; Angel Garcia de la Garza, UPenn ; Tyler M Moore, UPenn ; David Roalf, University of Pennsylvania ; Kosha Ruparel, University of Pennsylvania ; Daniel H Wolf, UPenn ; Raquel E Gur, University of Pennsylvania ; Ruben C Gur, University of Pennsylvania ; Danielle S Bassett, UPenn
10:55 AM	Discovery of Structural Brain Imaging Markers of HIV-Associated Outcomes Using Connectivity-Informed Regularization Approach — Jaroslaw Harezlak, Indiana University School of Public Health ; Damian Brzyski, Indiana University ; Marta Karas, Indiana University ; Joaquin Goni, Purdue University ; Beau Ances, Washington University School of Medicine ; Timothy Randolph, Fred Hutchinson Cancer Research Center
11:15 AM	Segmentation of Longitudinal Images Using Total Variation Regulation — Yuan Wang, Washington State University
11:35 AM	Computational Learning Methods for Neuroimaging Data Analysis — Don Hong, Middle Tennessee State Univ ; Xin Yang, Southern Arkansas University ; Jingsai Liang, Middle Tennessee State University
11:55 AM	An Integrative Model for Assessing Multimodal Neuroimaging Signatures of Post-Traumatic Stress Disorder — Fengqing Zhang, Drexel University ; Xin Niu, Drexel University
12:15 PM	Floor Discussion

152 !	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-306
Recent Development in Sufficient Dimension Reduction — Topic Contributed Papers
International Statistical Institute , Section on Statistical Learning and Data Science
Organizer(s): Qin Wang, Virginia Commonwealth University
Chair(s): Qiong Zhang, Virginia Commonwealth University
10:35 AM	On Sufficient Dimension Reduction with Missing Responses Through Estimating Equations — Yuexiao Dong, Temple University ; Qi Xia, Temple University ; Chengyong Tang, Temple University
10:55 AM	Simultaneous Variable Selection and Structural Dimension Estimation in Sufficient Dimension Reduction — Haileab Hilafu, University of Tennessee ; Wenbo Wu, University of Oregon
11:15 AM	Learning Causal Networks via Additive Faithfulness — Kuang-Yao Lee, Yale University ; Tianqi Liu, Yale University ; Bing Li, Pennsylvania State University ; Hongyu Zhao, Yale University
11:35 AM	Efficient Sparse Estimate of Sufficient Dimension Reduction in High Dimension — Wenhui Sheng ; Xin Chen, National University of Singapore ; Xiangrong Yin, University of Kentucky
11:55 AM	An Adaptive Approach to Dimension Reduction — Qin Wang, Virginia Commonwealth University
12:15 PM	Floor Discussion

159	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-322
SPEED: Sports and Business — Contributed Speed
Section on Statistics in Sports , Business and Economic Statistics Section , Section on Statistical Learning and Data Science
Chair(s): Michael Rutter, Penn State Erie
10:40 AM	Player Tracking for Division I Women's College Hockey — Maxime Bost-Brown ; Michael Schuckers, St. Lawrence University
10:45 AM	Predicting NFL Player Success From Analysts' Diction — Hubert Song, North Carolina State University ; Karl Pazdernik, North Carolina State University
10:50 AM	No Punt Intended — Nicholas Eisemann, California State Univ Chico ; Camille Pensabene, St. John Fisher College ; Dylan Gouthro, Chico State ; Demond Handley, Kansas State University ; MyDoris Soto, California State University-Fullerton
10:55 AM	Quantifying the Causal Effects of Conservative Fourth Down Decision Making in the National Football League — Derrick Yam, Skidmore College ; Michael J. Lopez, Skidmore College
11:05 AM	Predict NBA 2016-2017 Regular Season MVP Winner — Mason Chen, Mason Chen Consulting
11:10 AM	Predict NBA 2016-2017 Regular Season Team Winning% — Timothy Liu ; Mason Chen, Mason Chen Consulting
11:30 AM	Operations Research on NCAA Football Re-Injury Prevention — Nelson Chung, U. S. Census Bureau
11:40 AM	Functional Clustering of Elo Ratings in Professional Soccer Leagues — jinglin feng, Pennsylvania State University ; Andrew Hwang, Pennsylvania State University
11:45 AM	Instrumental Variables Approaches in Hurdle Data — Jacqueline Mauro, Carnegie Mellon University
11:50 AM	Deep Learning Econometrics — Guanhao Feng ; Nicholas Polson, University of Chicago ; Jianeng Xu, University of Chicago
11:55 AM	Unexpected Customer Relationship Leads to Doubling Market Share Through Predictive Analytics and Data Mining — Steven Reagan, L&L Products, Inc.
12:00 PM	How Statistics Is Essential to Business Analytics — Mary Whiteside, University of Texas-Arlington ; Mark Eakin, The University of Texas at Arlington
12:05 PM	Finding the Edge in Baccarat's Dragon and Panda Side Bets — Robert Hannum, University of Denver ; Teresa Dalton, University of California, Irvine
12:10 PM	Floor Discussion

172	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-345
Machine Learning and Algorithms — Contributed Papers
Section on Statistical Computing , Section on Statistical Learning and Data Science
Chair(s): Nicholas Meyer, North Carolina State University
10:35 AM	There Has to Be an Easier Way: a Simple Alternative for Parameter Tuning of Supervised Learning Methods — Jill Lundell
10:50 AM	Streaming Matrix Completion with Automated Rank Selection — Milo Page, JMP/NCSU ; Christopher M Gotwalt, JMP ; Alyson Wilson, North Carolina State University
11:05 AM	On Data Integration Problems with Manifolds — Kenneth Ryan, WVU ; Mark Culp, West Virginia University
11:20 AM	A Moment-Based Estimation Procedure for Deeply-Nested Hierarchical Models — Ningshan Zhang, New York University ; Patrick Perry, New York University ; Kyle Schmaus, Stitch Fix
11:35 AM	Online Anomaly Detection on Multi-Gigabit Network Streams — Zheng Gao, University of Michigan
11:50 AM	Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models — Daniel Apley, Northwestern University
12:05 PM	Matrix-Free Computation of Spatial-Temporal Gaussian Autoregressions and Related Stat-Space Models — Chunxiao Wang, Oregon State University ; Debashis Mondal, Oregon State University

174	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-346
Dynamic Network Modeling — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Tianxi Li
10:35 AM	Exploring Taxi Cab Networks Over Time with a Dynamic Stochastic Block Model — Beau Dabbs, Lawrence Livermore National Laboratory ; Giuliana Pallotta, Lawrence Livermore National Laboratory ; Goran Konjevod, Lawrence Livermore National Laboratory
10:50 AM	Modeling and Estimation of Contagion-Based Social Network Dependence with Time-To-Event Data — Lin Yu, North Carolina State University ; Wenbin Lu, North Carolina State University
11:05 AM	Estimation of Parameters in a Class of Dynamic Network Models — Wei Zhao, North Carolina State University
11:20 AM	Testing and Estimation of Social Network Dependence with Time-To-Event Data — Lin Su, North Carolina State University ; Wenbin Lu, North Carolina State University ; Rui Song, NC State University ; Danyang Huang, Renmin University
11:35 AM	Dynamic Network Monitoring Using Random Graph Theory and Statistical Process Monitoring — James D. Wilson, University of San Francisco ; Nathaniel Stevens, University of San Francisco ; William H Woodall, Virginia Tech
11:50 AM	Network Inference from Time Varying Grouped Observations — Yunpeng Zhao, George Mason University
12:05 PM	A Fast Scalable Procedure for Finding Change Points in Random Graphs — Mingyuan Gao, University of Florida ; George Michailidis, University of Florida

175	Mon, 7/31/2017, 10:30 AM - 12:20 PM	CC-Halls A&B
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Jessi Cisewski, Yale University
1:	Mixture DOE Approach for Weights Optimization in Regression Ensembles — Stanislav Zakharkin, PepsiCo
2:	Gaussian Mixture Models as Automated Particle Classifiers for Fast Neutron Detectors — Brenton Blair, Lawrence Livermore National Laboratory ; Ron Wurtz, Lawrence Livermore National Laboratory
3:	Model-Based Clustering with Application of Copula for Symbolic Data — Wenhao Pan, University of Georgia ; lynne Billard, University of Georgia
4:	Identifying clusters of cognitive functioning trajectories in elderly: A comparison of three methodologies — Victor Talisa, Department of Biostatistics, University of Pittsburgh ; Tianxiu Wang, University of Pittsburgh ; Zhongying Xu, University of Pittsburgh ; Joyce Chang, University of Pittsburgh
6:	Detecting Local Closeness of Two Distribution Functions — Zhun Deng, Harvard ; Jie Ding, Harvard ; Vahid Tarokh, Harvard
8:	Batch Policy Evaluation for Average Reward — Peng Liao ; Susan A Murphy, University of Michigan
9:	Optimal Variable Selection in Regression Models — Jie Ding, Harvard ; Vahid Tarokh, Harvard ; Yuhong Yang, University of Minnesota
10:	Variable Selection and Penalized Regression Methods for Clinical Projects: Practical Issues — Anne Eaton ; Mithat Gönen, Memorial Sloan Kettering Cancer Center
11:	AN ANALYSIS of NETWORK DISCUSSION TRENDS in TWITTER USING HASHTAG CLUSTERS — Elizabeth Tigner, Purdue University ; Jennifer Neville, Purdue University
12:	Periodicity in Game Theory — Michael Alexander Smith, Purdue University ; Bret Benesh, The College of St. Benedict/St. John's University ; Jamylle Carter, Diablo Valley College ; Deidra A. Coleman, Philander Smith College ; Jack Good, Purdue University ; Jennifer Travis, Lone Star College-North Harris ; Mark Ward, Purdue University
13:	Using Statistical Learning to Develop a More Sensitive Outcome for Progressive Multiple Sclerosis — Christopher Barbour, Montana State University ; Mark Greenwood, Montana State University ; Peter Kosa, National Institute of Neurological Disorders and Stroke, National Institutes of Health ; Danish Ghazali, National Institute of Neurological Disorders and Stroke, National Institutes of Health ; Makoto Tanigawa, National Institute of Neurological Disorders and Stroke, National Institutes of Health ; Blake Snyder, National Institute of Neurological Disorders and Stroke, National Institutes of Health ; Bibiana Bielekova, National Institute of Neurological Disorders and Stroke, National Institutes of Health
15:	CAM2 Network Camera Object Detection Dataset and Analysis — Kent Gauen, Purdue University ; Yuxiang Zi, Purdue University ; John Laiman, Purdue University ; Nirmal Asokan, Purdue University ; Yung-Hsiang Lu, Purdue University
16:	Optimization and Testing in Generalized Independent Component Analysis — Ze Jin, Cornell University ; David S Matteson, Cornell University
17:	Sequential Graph Matching and Streaming Sequential Monte Carlo — Seong-Hwan Jun
18:	Spline Density Estimation and Inference with Model-Based Penalties — Jian Shi, University of California, Santa Barbara
19:	Accelerated Failure Time Model with Log-Concave Error Distribution — Sunyul Kim, Sungkyunkwan university statstics department ; Byung tae Seo, Sungkyunkwan University

184	Mon, 7/31/2017, 11:35 AM - 12:20 PM	CC-Halls A&B
SPEED: Variable Selection and Networks — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Jessi Cisewski, Yale University
2:	A Robust Model-Free Feature Screening Method for Ultrahigh-Dimensional Data — Jingnan Xue, Texas A&M University ; Faming Liang, University of Florida
3:	Dynamic Latent Factor Modeling of UN Voting Networks — Bomin Kim, Pennsylvania State University ; Xiaoyue Niu, Penn State University ; David Hunter, The Pennsylvania State University ; Xun Cao, The Pennsylvania State University
4:	Variable Selection via Phony Variables — Wenhao Hu, North Carolina State University ; Eric Laber, North Carolina State University ; Leonard Stefanski, North Carolina State University
5:	Impact of Divergence of Training and Testing Sets on Predictive Risk and Measure of Model Complexity — Jieyi Jiang, Ohio State University ; Yoonkyung Lee, The Ohio State University ; Steven MacEachern, The Ohio State University
6:	Bayesian Adjustment for Confounding When Estimating Average Causal Effects for Time-To-Event Outcomes — Li Xu ; Arnold Stromberg, Department of Statistics, University of Kentucky ; Chi Wang, Cancer Biostatistics, University of Kentucky
7:	Efficient causal structure learning in high dimensions — Arjun Sondhi, University of Washington ; Ali Shojaie, University of Washington
8:	Assessing Variable Importance Nonparametrically Using Machine Learning Techniques — Brian Williamson, University of Washington ; Marco Carone, University of Washington Department of Biostatistics ; Noah Simon, University of Washington ; Peter Gilbert, Fred Hutchinson Cancer Research Center
9:	Varying-Coefficient Models for Dynamic Networks — Jihui Lee, Columbia University ; Gen Li, Columbia University ; James D. Wilson, University of San Francisco
10:	Parsimonious and Efficient Construction of Composite Likelihood Equations by L1-Penalization — Zhendong Huang
11:	Cross Validation for Penalized M-Estimation with a Case-Weight Adjusted Solution Path — Shanshan Tu, The Ohio State University ; Yunzhang Zhu, Ohio State University ; Yoonkyung Lee, The Ohio State University
12:	Finite Sample Estimation in General Vector Autoregressive Processes — Mohamad Kazem Shirani Faradonbeh, University of Michigan ; Ambuj Tewari, University of Michigan ; George Michailidis, University of Florida
13:	A Regularization Method for Detecting Differential Item Functioning Under the Framework of Generalized Linear Models — Jing Jiang, Boston College ; Zhushan Li, Boston College
14:	Collaborative Spectral Clustering in Attributed Networks — Xiaodong Jiang ; Pengsheng Ji, University of Georgia
15:	Structural Discovery in Temporal Networks — Shaojun Zhang, University of Florida ; George Michailidis, University of Florida
16:	Variable Selection for High-Dimensional Data via Generalized Penalty — Mingwei Sun, University of Alabama ; Pu Patrick Wang, University of Alabama
17:	Neighborhood Selection with Application to Social Networks — Nana Wang ; Wolfgang Polonik, University of California, Davis
18:	Detection of Treatment Effect After Variable Selection Under Model Misspecification — Jingshen Wang, University of Michigan ; Xuming He, University of Michigan
19:	Fostering Undergraduate Data Science — Mark Ward, Purdue University ; Fulya Gokalp Yavuz, Purdue University and Yildiz Technical University
20:	Model-Based Community Detection for Networks with Node Covariates — Boang Liu, University of Michigan ; Ji Zhu, University of Michigan
The Speed portion will take place during Session 214519

185	Mon, 7/31/2017, 11:35 AM - 12:20 PM	CC-Halls A&B
SPEED: Environmental Statistics — Contributed Poster Presentations
Section on Statistics and the Environment , Section on Statistical Learning and Data Science , Statistics Without Borders
Chair(s): Jessi Cisewski, Yale University
21:	'On-the-Fly' Goodness of Fit and Outlier Testing for Left-Censored Data — Kirk Cameron, MacStat Consulting, Ltd.
22:	Spatially Varying Autoregressive Models for Prediction of New HIV Diagnoses — Lyndsay Shand ; Bo Li, University of Illinois
23:	Modeling Spatial Extremes Using Positive Stable Mixtures — Gregory Bopp
24:	Computer Model Calibration via the Ensemble Kalman Filter — Seiyon Lee, Pennsylvania State University ; Murali Haran, Pennsylvania State University ; Klaus Keller, Pennsylvania State University
25:	Unsupervised Self-Normalized Change-Point Testing for Time Series — Liliya Lavitas, Boston University ; Ting Zhang, Boston University
26:	Migratory Bird Surveys and the Modeling of Seabird Populations — Robert Fowler
27:	Extreme Value Based Methods for Modeling Elk Dispersal — Dhanushi Wijeyakulasuriya, Pennsylvania State University ; Ephraim M Hanks, The Pennsylvania State University ; Benjamin A Shaby, Penn State University
28:	Single index model for inhomogeneous spatial point processes — Ji Meng Loh ; Yixin Fang, New Jersey Institute of Technology
29:	Copula-Based Estimation for Markov Models with Detection Limits — Fuyuan Li, George Washington University
30:	Rank-Based Estimation for Generalized Additive Models with an Application to Fisheries Data — Hannah Correia, Auburn University ; Asheber Abebe, Auburn University
31:	Estimates of Extreme Rainfall Frequency from Spatially Dense Observations — Lynne Seymour, University of Georgia ; Kyle Mattingly, University of Georgia ; Paul Miller, University of Georgia
32:	Using generalized linear models to refine management of marten trap lines — Alyssa Crawford, Alaska Department of Fish and Game
33:	Green Power Statistics: Local Wind Speed Modeling as Basis for Wind Turbine Performance Prediction — Marina Nechayeva, LaGuardia Community College ; Malgorzata Marciniak, LaGuardia Community College ; Vladimir Przhebelskiy , LaGuardia Community College ; Michael Wiley, LaGuardia Community College ; Paul DeVries, LaGuardia Community College
35:	Constrained Functional Clustering of Arctic Sea Ice Extent Data — Tan Tran, Montana State University ; Christopher Barbour, Montana State University ; Mark Greenwood, Montana State University
The Speed portion will take place during Session 214532

204 * !	Mon, 7/31/2017, 2:00 PM - 3:50 PM	CC-308
Recent Development in Statistical Methods for Analyzing Big and Complex Neuroimaging Data — Invited Papers
ENAR , Section on Statistics in Imaging , Section on Statistical Learning and Data Science
Organizer(s): Jian Kang, University of Michigan, Yize Zhao, Weill Cornell Medicine, Cornell University
Chair(s): Yize Zhao, Weill Cornell Medicine, Cornell University
2:05 PM	Recent Development in Statistical Methods for Analyzing Big and Complex Neuroimaging Data — Ciprian M Crainiceanu, Johns Hopkins University
2:30 PM	High-Dimensional Multivariate Mediation with Application to Neuroimaging Data — Martin Lindquist, Johns Hopkins University
2:55 PM	Functional Data Modeling of Dynamic PET Data — R. Todd Ogden, Department of Biostatistics, Columbia University ; Yakuan Chen, Columbia University ; Jeff Goldsmith, Columbia University
3:20 PM	Functional Regression Models for Nonignorable Missing Scalar Responses — Hongtu Zhu, The University of Texas MD Anderson Cancer Center ; Tengfei Li, MD Anderson
3:45 PM	Floor Discussion

207 !	Mon, 7/31/2017, 2:00 PM - 3:50 PM	CC-338
Experiments and Inference for Social Networks — Invited Papers
Section on Statistical Computing , Section on Statistical Learning and Data Science , Section on Statistics in Marketing , Statistics in Business Schools Interest Group
Organizer(s): Daniel L Sussman, Boston University
Chair(s): Elizabeth L Ogburn, Johns Hopkins University
2:05 PM	Massive Meta-Analysis with Experiments as Instruments: Applications to Peer Effects in Networks — Dean Eckles, MIT ; Alexander Peysakhovich, Facebook
2:30 PM	Unbiased Estimation Under Network Interference — Daniel L Sussman, Boston University ; Edoardo M. Airoldi, Harvard University
2:55 PM	Design of Experiments for Networks with Interference — Alexander Volfovsky, Duke University
3:20 PM	A Computational Perspective of Information Propagation in Social Networks — Laks V.S. Lakshmanan, University of British Columbia
3:45 PM	Floor Discussion

219 * !	Mon, 7/31/2017, 2:00 PM - 3:50 PM	CC-349
SLDS 2017 Student Paper Awards Session — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Tian Zheng, Department of Statistics, Columbia University
Chair(s): Mengling Liu, New York University School of Medicine
2:05 PM	Graph-Based Sparse Linear Discriminant Analysis for High-Dimensional Classification — Jianyu Liu, University of North Carolina at Chapel Hill ; Guan Yu, State University of New York at Buffalo ; Yufeng Liu, University of North Carolina
2:25 PM	Statistical Inference for Model Parameters in Stochastic Gradient Descent — Yichen Zhang, New York University ; Xi Chen, NYU ; Jason D. Lee, University of Southern California ; Tong Thomson Xin, National University of Singapore
2:45 PM	Composite Interaction Tree for Robust Learning of Optimal Individualized Treatment Rules and Identifying Subgroups — Xin Qiu, Columbia University ; Yuanjia Wang , Columbia University
3:05 PM	Unified Methods for Variable Selection in Large-Scale Genomic Studies with Censored Survival Outcomes — Lauren Spirko, Temple University ; Karthik Devarajan, Fox Chase Cancer Center
3:25 PM	Individualized Multilayer Tensor Learning with an Application in Imaging Analysis — Xiwei Tang, University of Illinois at Urbana-Champaign ; Xuan Bi, Yale University ; Annie Qu, University of Illinois at Urbana-Champaign
3:45 PM	Floor Discussion

237	Mon, 7/31/2017, 2:00 PM - 3:50 PM	CC-313
Feature Selection and Statistical Learning in Genomics — Contributed Papers
Section on Statistics in Genomics and Genetics , Section on Statistical Learning and Data Science
Chair(s): Arjun Sondhi, University of Washington
2:05 PM	FUNctioNal ELastic-Net (FUNNEL) Pipeline for Gene Set Enrichment Analysis with Overlapping — Yun Zhang, University of Rochester ; Juilee Thakar, University of Rochester ; Xing Qiu, University of Rochester
2:20 PM	Group Variable Selection with Compositional Covariates — Anna Plantinga, University of Washington ; Michael C. Wu, Fred Hutchinson Cancer Research Center
2:35 PM	Cancer Staging in Meta-Analysis by Group Fused Lasso — Wenshuo Liu, University of Wisconsin Madison ; Tianjie Wang, University of Wisconsin Madison ; Menggang Yu, university of wisconsin-madison
2:50 PM	Strong Sure Screening of Ultra-High-Dimensional Categorical Data Exhibiting Trend — Randall Reese, Utah State University ; Guifang Fu, Utah State University ; Xiaotian Dai, Utah State University
3:05 PM	Robust Network-Based Regularization and Variable Selection for High-Dimensional Genomic Data in Cancer Prognosis — Jie Ren, Department of Statistics, Kansas State University ; Yinhao Du, Department of Statistics, Kansas State University ; Dewey Molenda, Department of Statistics, Kansas State University ; Yu Jiang, School of Public Health, University of Memphis ; Wu Cen, Department of Statistics, Kansas State University
3:20 PM	Graph Constrained Regularization for Nonparametric Instrumental Variable Regression in Genetical Genomic Analysis — Bin Gao, Janssen Research & Development, LLC ; Yuehua Cui, Michigan State University
3:35 PM	Floor Discussion

250	Mon, 7/31/2017, 2:00 PM - 2:45 PM	CC-Halls A&B
SPEED: Sports and Business — Contributed Poster Presentations
Section on Statistics in Sports , Business and Economic Statistics Section , Section on Statistics in Marketing , Section on Statistical Learning and Data Science
Chair(s): Jessi Cisewski, Yale University
2:	Player Tracking for Division I Women's College Hockey — Maxime Bost-Brown ; Michael Schuckers, St. Lawrence University
3:	Predicting NFL Player Success From Analysts' Diction — Hubert Song, North Carolina State University ; Karl Pazdernik, North Carolina State University
4:	No Punt Intended — Nicholas Eisemann, California State Univ Chico ; Camille Pensabene, St. John Fisher College ; Dylan Gouthro, Chico State ; Demond Handley, Kansas State University ; MyDoris Soto, California State University-Fullerton
5:	Quantifying the Causal Effects of Conservative Fourth Down Decision Making in the National Football League — Derrick Yam, Skidmore College ; Michael J. Lopez, Skidmore College
7:	Predict NBA 2016-2017 Regular Season MVP Winner — Mason Chen, Mason Chen Consulting
8:	Predict NBA 2016-2017 Regular Season Team Winning% — Timothy Liu ; Mason Chen, Mason Chen Consulting
11:	Operations Research on NCAA Football Re-Injury Prevention — Nelson Chung, U. S. Census Bureau
13:	Functional Clustering of Elo Ratings for Competitive Balance Analysis in Professional Soccer Leagues — jinglin feng, Pennsylvania State University ; Andrew Hwang, Pennsylvania State University
14:	Instrumental Variables Approaches in Hurdle Data — Jacqueline Mauro, Carnegie Mellon University
15:	Deep Learning Econometrics — Guanhao Feng ; Nicholas Polson, University of Chicago ; Jianeng Xu, University of Chicago
16:	Unexpected Customer Relationship Leads to Doubling Market Share Through Predictive Analytics and Data Mining — Steven Reagan, L&L Products, Inc.
17:	How Statistics Is Essential to Business Analytics — Mary Whiteside, University of Texas-Arlington ; Mark Eakin, The University of Texas at Arlington
18:	Finding the Edge in Baccarat's Dragon and Panda Side Bets — Robert Hannum, University of Denver ; Teresa Dalton, University of California, Irvine
The Speed portion will take place during Session 214525

265	Tue, 8/1/2017, 8:30 AM - 10:20 AM	CC-309
New Directions in Statistical Network Analysis — Invited Papers
Section on Statistical Learning and Data Science , IMS , Section on Nonparametric Statistics
Organizer(s): Liza Levina, University of Michigan
Chair(s): Liza Levina, University of Michigan
8:35 AM	Structured Shrinkage for Network Regression — Peter Hoff, Duke University
9:00 AM	Prediction Models for Network-Linked Data — Ji Zhu, University of Michigan
9:25 AM	Network modelling of topological domains using Hi-C data — Rachel Wang ; Purnamrita Sarkar, University of Texas, Austin ; Oana Ursu, Stanford University ; Anshul Kundaje, Stanford University ; Peter Bickel, University of California, Berkeley
9:50 AM	Discussant: Edoardo M. Airoldi, Harvard University
10:15 AM	Floor Discussion

268 * !	Tue, 8/1/2017, 8:30 AM - 10:20 AM	CC-349
A Unifying Theme for Interpretable Information Extraction from Data: The Stability Principle — Invited Papers
IMS , Section on Statistical Learning and Data Science
Organizer(s): Bin Yu, University of California, Berkeley
Chair(s): Anru Zhang, University of Wisconsin-Madison
8:35 AM	Three Principles for Data Science: Predictability, Stability and Computability — Bin Yu, University of California, Berkeley
9:00 AM	Stability, Uncertainty, and Bayesian Learning — Chris Holmes, University of Oxford
9:25 AM	Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing — Ryan Rogers, University of Pennsylvania ; Aaron Roth, University of Pennsylvania ; Adam Smith, Pennsylvania State University ; Om Thakkar, Penn State
9:50 AM	The Central Role of Stability in Causal Inference — Peng Ding, University of California, Berkeley
10:15 AM	Floor Discussion

269 * !	Tue, 8/1/2017, 8:30 AM - 10:20 AM	CC-343
Bayesian Models for Population Mobility: Current Developments and Future Directions — Invited Papers
Section on Bayesian Statistical Science , Section on Statistics in Epidemiology , Section on Statistical Learning and Data Science
Organizer(s): Adrian Dobra, University of Washington
Chair(s): Adrian Dobra, University of Washington
8:35 AM	Statistical Methods for Ecological Networks — Catherine Calder, The Ohio State University ; Christopher Browning, The Ohio State University
9:05 AM	SAGA: Socially- and Geography-Aware Mobility Modeling Framework — Abel Rodriguez, University of California, Santa Cruz
9:35 AM	Predicting Travel Time Reliability Using Mobile Phone GPS Data — Dawn Woodard, Uber Technologies, Inc.
10:05 AM	Floor Discussion

291	Tue, 8/1/2017, 8:30 AM - 10:20 AM	CC-322
SPEED: Biopharmaceutical Statistics — Contributed Speed
Biopharmaceutical Section , Section on Statistical Learning and Data Science
Chair(s): Veronica L. Powell, QST Consultations
8:35 AM	Tradeoffs of a Randomize, Then Consent Approach to Improving Cluster Participation Rates in Cluster Randomize Trials — Abigail Shoben, The Ohio State University
8:40 AM	Sample Size for Joint Testing Cause-Specific Hazard and Overall Hazard in the Presence of Competing Risks — Qing Yang, Duke University ; Gang Li, University of California, Los Angeles
8:45 AM	Accounting for Baseline Covariates and Missing Data in Regulatory Trials with Longitudinal Designs — Elizabeth Colantuoni, Johns Hopkins University ; Jon Steingrimsson, Johns Hopkins University ; Aidan McDermott, Johns Hopkins University ; Michael Rosenblum, Johns Hopkins University
8:50 AM	Robust Feature Selection and Cell Line Classification with Electric Cell-Substrate Impedance Sensing Data — Megan Gelsinger, Cornell University ; David S Matteson, Cornell University ; Laurie Tupper, Williams College
8:55 AM	Comparing Biomarker-Guided Treatment Strategies Using Local Posterior Predictive Benefit — Meilin Huang, The University of Texas MD Anderson Cancer Center ; Brian P. Hobbs, The University of Texas MD Anderson Cancer Center
9:00 AM	A Comparison of Assay Platforms Using Correlation Coefficients in the Presence of Repeated Measurements — Qinlei Huang, Merck & Co. ; Radha Railkar, Merck & Co. ; Anita Lee, Merck & Co.
9:10 AM	On Measure of Surrogacy for Biomarkers in Medical Research. — Rui Zhuang, University of Washington ; Ying Qing Chen, Fred Hutchinson Cancer Research Center
9:15 AM	Discriminant Analysis (DA) Based Methods in Safety Evaluation — Angang Zhang, Merck ; Richard Baumgartner, Merck ; William W Wang, Merck & Co Inc
9:20 AM	On the Estimation of Risk Difference in the Presence of Continuous Baseline Covariates — Hua Ma, Merck ; Robin Mogg, Merck
9:30 AM	Power Comparison of Tests of Restricted Mean Survival Time with Log-Rank Test and Generalized Wilcoxon Test Under Various Survival Distributions — Musashi Fukuda, Astellas Pharma Inc. ; Yutaka Matsuyama, Department of Biostatistics, School of Public Health, The University of Tokyo
9:35 AM	Optimal Designs for Pharmacokinetic Studies Analyzed Using Non-Compartmental Methods — Helen Barnett, Lancaster University ; Thomas Jaki, Lancaster University ; Helena Geys, Janssen Pharmaceutica ; Tom Jacobs, Janssen Pharmaceutica
9:40 AM	Statistical Considerations in Delayed-Start Design to Demonstrate Disease Modification Effect in Neurodegenerative Disorders — Jun Zhao, AbbVie Inc. ; Deli Wang, AbbVie, Inc. ; Weining Z Robieson, AbbVie Inc.
9:45 AM	Placebo-Based Multiple Imputation Methods for Sensitivity Analysis in Recurrent Event Data — Rui Yang, Chiltern International Inc
9:50 AM	Sample Size Calculation for the Generalized Poisson Regression Model Comparing Two Rates of Recurrent Events — Kimitoshi Ikeda, Amgen Astellas BioPharma K.K.
9:55 AM	Sample Size Calculation to Support Local Submission — Zhuqing Yu, AbbVie ; Bidan Huang, AbbVie ; Jun Zhao, AbbVie Inc. ; lu cui, Abbvie
10:00 AM	An Examination of the Association Between Alcohol and Dementia in a Longitudinal Study — Tingting Hu, Florida State University ; Dan McGee, Florida State University ; Elizabeth Slate, Florida State University
10:05 AM	Design and Statistical Analysis of Method Transfer Studies for Biotechnology Products — Meiyu Shen, C DER, FDA ; Lixin Xu, FDA
10:15 AM	Floor Discussion

293	Tue, 8/1/2017, 8:30 AM - 10:20 AM	CC-326
SPEED: Computing, Graphics, and Programming Statistics — Contributed Speed
Section on Statistical Computing , Section on Statistical Graphics , Section for Statistical Programmers and Analysts , Quality and Productivity Section , Section on Statistical Consulting , Section on Statistical Learning and Data Science
Chair(s): Collin Eubanks, Carnegie Mellon University
8:35 AM	Where Does All the Time Go? Measuring Clients' Service Utilization via Time-Tracking Software — Dou-Yan Yang, University of Wisconsin--Madison ; Jeff Havlena, University of Wisconsin--Madison
8:40 AM	Persistence Terrace for Topological Inference of Point Cloud Data — Chul Moon, University of Georgia ; Noah Giansiracusa, Swarthmore College ; Nicole Lazar, University of Georgia
8:50 AM	Generalized Bi-Plots for Visualized Multidimensional Projections — James Fry, Virginia Tech ; Matt Slifko, Virginia Tech ; Scotland Leman, Virginia Tech
8:55 AM	Dynamic Network Community Discovery — Shiwen Shen ; Edsel Aldea Pena, University of South Carolina
9:05 AM	Maintaining Trial Integrity for Randomized Open-Label Trials — Wenyun Ji, Amgen, Inc.
9:10 AM	A Mixture-Of-Regressions Model with Measurement Errors in the Response — Xiaoqiong Fang, University of Kentucky ; Derek Young, University of Kentucky
9:15 AM	A SAS Macro to Create Summary Tables — Amy Gravely, VA Medical Center ; Barbara Clothier, CCDOR MVAHCS
9:20 AM	Sequential Computer Experiments for Failure Probability Estimation --- a Floor System Example — Hao Chen, University of British Columbia ; William James Welch, University of British Columbia
9:30 AM	A Network-based Algorithm for Clustering Multivariate Longitudinal Data — Matthew Koslovsky, KBRwyle ; Millennia Young, National Aeronautics and Space Administration ; Caroline Schaefer, MEI Technologies ; John Arellano, MEI Technologies ; Al Feiveson, National Aeronautics and Space Administration
9:35 AM	Floor Discussion

294	Tue, 8/1/2017, 8:30 AM - 10:20 AM	CC-330
High-Dimensional Regression — Contributed Papers
Biometrics Section , Section on Statistics in Genomics and Genetics , Section on Statistical Learning and Data Science
Chair(s): Roberta De Vito, Princeton
8:35 AM	High-Dimensional Discriminant Analysis Using Singular Wishart Distribution — Samprit Banerjee, Weill Medicine College of Cornell University ; Stefano Monni, American University of Beirut, Lebanon
8:50 AM	High-Dimensional Mediation Analysis with Latent Factors — Andriy Derkach, NIH-National Cancer Institute ; Ting-Huei Chen, Université Laval ; Joshua Sampson, National Cancer Institute ; Ruth Pfeiffer , National Cancer Institute, NIH, HHS
9:05 AM	A focused mean squared error approach for selecting tuning parameters in penalized regression — Kristoffer Hellton, University of Oslo ; Nils Lid Hjort, University of Oslo
9:20 AM	An Application of High-Dimensional Multiclass Classification Methods to Listeria Monocytogenes Whole Genome Multilocus Sequence Typing Data — Sunkyung Kim, Centers for Disease Control and Prevention ; Gordana Derado, Centers for Disease Control and Prevention ; Anna J Blackstock, Centers for Disease Control and Prevention ; Conrad Amanda, Centers for Disease Control and Prevention ; Heather Carleton, Centers for Disease Control and Prevention
9:35 AM	Honest inference for marginal treatment effects using penalised bias-reduced double-robust estimation — Vahe Avagyan, Universiteit Gent-Vakgroep Toegepaste W & I ; Stijn Vansteelandt, Ghent University
9:50 AM	Dynamic Predictions in Bayesian Functional Joint Models for Longitudinal and Time-To-Event Data: An Application to Alzheimer's Disease — Kan Li, The University of Texas Health Science Center at Houston ; Sheng Luo, The University of Texas Health Science Center at Houston
10:05 AM	Multinomial Goodness-of-Fit Statistics When the Number of Variables Is Large — Maduranga Dassanayake, Arizona State University ; Mark Reiser, Arizona State University

304	Tue, 8/1/2017, 8:30 AM - 10:20 AM	CC-313
Statistical Learning: Dimension Reduction — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Vadim Zipunnikov, Johns Hopkins University
8:35 AM	Supervised Dimension Reduction with Application to Driver Gene Detection — Yichen Cheng
8:50 AM	Sparse Principal Component Analysis with Missing Observations — Seyoung Park ; Hongyu Zhao, Yale University
9:05 AM	On the Similarity of Principal Components, Random Projections and Random Column Subsampling for Dimension Reduction in High-Dimensional Linear Regression — Martin Slawski, George Mason Univ
9:20 AM	Information Tests on Statistical Submanifolds — Michael Trosset, Indiana University ; Carey E Priebe, Johns Hopkins University
9:35 AM	Dimension Selection for Two-Step Linear Discriminant Analysis — Ting-Li Chen, Institute of Statistical Sciences, Academia Sinica ; Yi-Heng Sun, National Taiwan University
9:50 AM	Regularized Discriminant Analysis in Presence of Cellwise Contamination — Stephanie Aerts, University of Liège ; Ines Wilms, KU Leuven
10:05 AM	Sequential Co-Sparse Factor Regression — Aditya Mishra, University of Connecticut ; Kun Chen, Department of Statistics, University of Connecticut ; Dipak K Dey, university of connecticut

307 * !	Tue, 8/1/2017, 10:30 AM - 12:20 PM	CC-344
Bayesian Computational Advances for Complex and Large-Scale Data — Invited Papers
Section on Statistical Computing , Section on Bayesian Statistical Science , Section on Statistical Learning and Data Science
Organizer(s): Naveen N Narisetty, University of Illinois at Urbana-Champaign
Chair(s): Alexander Giessing, University of Michigan
10:35 AM	Bayesian Neural Networks for High-Dimensional Nonlinear Variable Selection — Faming Liang, University of Florida
11:00 AM	Multivariate Output Analysis for Markov Chain Monte Carlo — Galin Jones, University of Minnesota ; Dootika Vats, University of Minnesota ; James Flegal, University of California, Riverside
11:25 AM	High-Dimensional Bayesian Computation for Censored Quantile Regression — Naveen N Narisetty, University of Illinois at Urbana-Champaign ; Xuming He, University of Michigan
11:50 AM	A Variational Bayesian Algorithm for Sparse PCA — Feng Liang, University of Illinois at Urbana Champaign ; Yunbo Ouyang, University of Illinois at Urbana Champaign ; Jianjun Hu, Union Bank
12:15 PM	Floor Discussion

309 *	Tue, 8/1/2017, 10:30 AM - 12:20 PM	CC-331/332
Statistical Topics in Precision Medicine — Invited Papers
IMS , ENAR , Section on Statistical Learning and Data Science
Organizer(s): Heping Zhang, Yale University School of Public Health
Chair(s): Heping Zhang, Yale University School of Public Health
10:35 AM	New Adaptive Designs of Clinical Trial for Precision Medicine — Feifang Hu, George Washington University
11:00 AM	Individualized Fusion Learning (IFusion) with Applications to Personalized Inference — Minge Xie, Rutgers University ; Jieli Shen, Rutgers University ; Regina Liu, Rutgers University
11:25 AM	Statistical Machine Learning and Precision Medicine — Michael Lawson, University of North Carolina at Chapel Hill ; Michael R Kosorok, University of North Carolina at Chapel Hill
11:50 AM	Hypothesis testings on high-dimensional individualized treatment rules — Young-Geun Choi, Fred Hutchinson Cancer Research Center ; Yang Ning, Cornell University ; Yingqi Zhao, Fred Hutchinson Cancer Research Center
12:15 PM	Floor Discussion

312 * !	Tue, 8/1/2017, 10:30 AM - 12:20 PM	CC-338
SAMSI-CCNS: Innovations and Challenges in Computational Neuroscience — Invited Papers
International Indian Statistical Association , Biometrics Section , ENAR , Section on Statistics in Imaging , Section on Statistical Learning and Data Science
Organizer(s): Sujit K. Ghosh, SAMSI
Chair(s): Sujit K Ghosh, North Carolina State University
10:35 AM	Population-Based Brain Structural Connectivity Analysis — Hongtu Zhu, The University of Texas MD Anderson Cancer Center ; Anuj Srivastava, Florida State University ; David B. Dunson, Duke University ; Maxime Descoteaux, Université de Sherbrooke ; Zhengwu Zhang, Duke University
11:00 AM	Ultra-High-Dimensional Genome-Wide Heritability Analysis with Neuroimaging Phenotypes — Yize Zhao, Weill Cornell Medicine, Cornell University
11:25 AM	A New Unified ICA Framework for Decomposing Multimodal Neuroimaging Data — Ying Guo, Emory University ; Subhadip Pal, Emory University ; Jian Kang, University of Michigan
11:50 AM	A LAG FUNCTIONAL LINEAR MODEL for PREDICTION of MAGNETIZATION TRANSFER RATIO in MULTIPLE SCLEROSIS LESIONS — Gina-Maria Pomann, Duke University ; Ana-Maria Staicu, North Carolina State University, Department of Statistics ; Edgar Lobaton, North Carolina State University ; Amanda Mejia, Indiana University ; Blake Dewey, NINDS ; Daniel Reich, NINDS ; Elizabeth M. Sweeney, Flatiron Health ; Russell Shinohara, University of Pennsylvania
12:15 PM	Floor Discussion

314	Tue, 8/1/2017, 10:30 AM - 12:20 PM	CC-329
Recent Advances in High-Dimensional Inferences — Invited Papers
IMS , International Chinese Statistical Association , Section on Statistical Learning and Data Science
Organizer(s): Ming Yuan, University of Wisconsin
Chair(s): Ming Yuan, University of Wisconsin
10:35 AM	Overlapping clustering with LOVE — Florentina Bunea, Cornell
11:00 AM	Adaptive Prediction in Additive Models — Cun-Hui Zhang, Rutgers University
11:25 AM	Rate-Optimal Perturbation Bounds for Singular Subspaces with Applications to High-Dimensional Data Analysis — Tony Cai, University of Pennsylvania ; Anru Zhang, University of Wisconsin-Madison
11:50 AM	Robust Covariate-Adjusted Multiple Testing — Jianqing Fan, Princeton University ; Wen-Xin Zhou, Princeton University ; Koushiki Bose, Princeton University ; Han Liu, Princeton University
12:15 PM	Floor Discussion

321	Tue, 8/1/2017, 10:30 AM - 12:20 PM	CC-346
Modern Statistical Learning for Ranking and Crowdsourcing — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Xi Chen, NYU
Chair(s): Xi Chen, NYU
10:35 AM	Top-K Rank Aggregation from Pairwise Comparisons — Yuxin Chen
10:55 AM	Optimal Stopping and Worker Selection in Crowdsourcing: An AdaptiveSequential Probability Ratio Test Framework — Xi Chen, NYU ; Xiaoou Li, University of Minnesota Twin Cities ; Jingcheng Liu, Columbia University ; Zhiliang Ying, Columbia University ; Yunxiao Chen, Emory University
11:15 AM	A Permutation-Based Model for Crowdsourcing: Optimal Estimation and Robustness — Nihar B Shah, Univ of California - Berkeley ; Sivaraman Balakrishnan, Department of Statistics, CMU ; Martin J. Wainwright, EECS and Statistics, University of California, Berkeley
11:35 AM	Sequential Rank Aggregation from Pairwise Comparison — Xiaoou Li, University of Minnesota Twin Cities ; Xi Chen, NYU ; Yunxiao Chen, Emory University ; Jingcheng Liu, Columbia University ; Zhiliang Ying, Columbia University
12:15 PM	Floor Discussion

334	Tue, 8/1/2017, 10:30 AM - 12:20 PM	CC-325
SPEED: Statistical Education — Contributed Speed
Section on Statistical Education , Section on Teaching of Statistics in the Health Sciences , Section on Statistical Learning and Data Science , Social Statistics Section
Chair(s): Gwendolyn Eadie, McMaster University
10:35 AM	Benefits of Using Real Data Sets to Instruct Business Students in Data Mining Techniques — Kathleen Garwood, Saint Joseph's University
10:40 AM	A prediction model for understanding statistical replication — Andrew Neath, SIU Edwardsville
10:45 AM	Triathlon Road Closure Control — Zonghuan (Jason) Li, Student ; Mason Chen, Mason Chen Consulting
10:50 AM	Using an Alternative Sequence for Teaching an Undergraduate Introductory Statistics — Phyllis Curtiss, Grand Valley State University ; Robert Pearson, Grand Valley State University
10:55 AM	Reflections and Faculty Feedback on an Alternative Sequence for Teaching an Undergraduate Introductory Statistics Course — Robert Pearson, Grand Valley State University ; Phyllis Curtiss, Grand Valley State University
11:00 AM	Statistics as a Basic Leadership Competency: Making the Case to Executives and Educators — Matthew Jones, Walden University
11:05 AM	Learning Statistics with Productive Practice and Technology — Brenda Gunderson, Univ of Michigan
11:10 AM	Applying Logistic Regression to Student Data to Determine Retention of Students at a Large University — Reema Thakkar, RTI International ; Emily Griffith, NC State University ; Stephany Dunstan, N.C. State University
11:15 AM	First (?) Occurrence of the 25 Most Influential (?) Statistical Terms Introduced in the Last 20 Years — John McKenzie, Babson College
11:20 AM	Developing Partnerships with an AP Statistics Practice Exam — Christy Brown, Clemson University ; Ellen Breazel, Clemson University ; Elizabeth Johnson, George Mason University ; Jonathan Duggins, NC State University ; Bryan Crissinger, University of Delaware
11:30 AM	Helping Under Prepared Students Succeed in Introductory Statistics — Paul Plummer, University of Central Missouri
11:35 AM	Using Video Presentations for Assessment in Introductory Statistics Courses — Melissa Pittard, University of Kentucky
11:40 AM	Longitudinal Modeling in Applied Research: Implications for Improving Practice — Niloofar Ramezani, University of Northern Colorado ; Kerry Duck, University of Northern Colorado ; Austin Brown, University of Northern Colorado ; Michael Floren, University of Northern Colorado ; Krystal Hinerman, Lamar University ; Trent Lalonde, University of Northern Colorado
11:45 AM	P-Value as Strength of Evidence Measured by Confidence Distribution — Sifan Liu, Rutgers Univ Statistics Dept
11:50 AM	Definition and Confusion About Independence — Robert Molnar, Oklahoma State University
11:55 AM	If Only R Would Grade My Students' Projects — Robin Lock, St. Lawrence University
12:00 PM	Odds Ratio Versus Risk Ratio in Prevalence Trend Analysis — Scott McClintock, West Chester University ; Randall Rieger, West Chester University ; Zhen-qiang Ma, Pennsylvania Department of Health
12:05 PM	Using Data Mining to Identify At-Risk Freshmen — Nora Galambos, Stony Brook University
12:10 PM	Assessing the Effectiveness of Mentoring Youth — Laura Albrecht ; Keenan O'brien, Metropolitan State University of Denver ; Matthew Shaw, Metropolitan State University of Denver
12:15 PM	Floor Discussion

335	Tue, 8/1/2017, 10:30 AM - 12:20 PM	CC-326
SPEED: Reliable Statistical Learning and Data Science — Contributed Speed
Section on Statistical Learning and Data Science , Section on Physical and Engineering Sciences , International Statistical Institute
Chair(s): Jacob Bien, Cornell University
10:35 AM	Estimating an Inverse Mean Subspace — Jiaying Weng ; Xiangrong Yin, University of Kentucky
10:40 AM	Variable Selection on Functional Data Using Kernel Machine — Haoyu Wang, North Carolina State University
10:45 AM	Expected Conditional HSIC for Testing Independence — Chenlu Ke ; Xiangrong Yin, University of Kentucky
10:50 AM	Nonlinear Mixed-Effects Mixture Models for Clustering Longitudinal Data with an Application — Chongshu Chen, University of Rochester
10:55 AM	Coefficient Estimation and Hypothesis Testing on Interval-Valued Data Regression by Measurement Error — Yaotong Cai, University of Georgia ; lynne Billard, University of Georgia
11:00 AM	Quantifying Uncertainty in Latent Dirichlet Allocation — Christine Chai, Duke University
11:05 AM	Learning from Imbalanced Data: a Review of Some Existing Methodologies — Josephine Akosa, Oklahoma State University ; Melinda McCann, Oklahoma State University
11:10 AM	Tuning Variable Selection via Noise When Prediction Is Not the Primary Objective — Eric Reyes, Rose-Hulman Institute of Technology ; Xiaomo Wang, Rose-Hulman Institute of Technology
11:15 AM	A Simulation Study to Evaluate Variable Importance Measures in Random and Conditional Inference Forests with Imputation for Data with Missing and Correlated Predictors — Hung-Wen Yeh ; Rayus Kuplicki, Laureate Institute for Brain Research ; Trang Le, University of Tulsa ; Martin P. Paulus, Laureate Institute for Brain Research
11:20 AM	Sparse Network Tomography for Anomaly Detection — Elizabeth Hou, University of Michigan ; Yason Yilmaz, University of South Florida ; Alfred Hero, University of Michigan
11:30 AM	High-Dimensional Discriminant Analysis for Spatially Correlated Data — Yingjie Li, Michigan State University ; Tapabrata Maiti, Michigan State University
11:40 AM	Linear Combinations of Percentiles for Tests in the One Way Layout — Yvonne Zubovic, Indiana University Purdue University Fort Wayne
11:45 AM	Multivariate Functional Data Clustering with Automatic Variable Selection — Zhongnan Jin ; Yili Hong, Virginia Tech ; Qingyu Yang, Department of Industrial and Systems Engineering Wayne State University
11:50 AM	Robust Mode Detection Schemes in a Non-Stationary Environment — Francois Alastair Marshall, Queen's University
11:55 AM	Statistical Challenges in Materials Mechanics: Microstructure to Performance — Scott Vander Wiel, Los Alamos National Laboratory
12:00 PM	A New Multivariate Measure of Agreement in Method-Comparison Studies — Sasiprapa Hiriote, Department of Statistics, Faculty of Science, SILPAKORN UNIVERSITY ; Vernon M Chinchilli, Department of Public Health Sciences, Penn State Hershey College of Medicine
12:05 PM	Using Bandit Algorithms on Changing Reward Rates — Jeffrey Roach, OpenMail
12:10 PM	Supervised Binning Techniques for Predictive Modeling — Zhen Zhang, C Spire ; Lei Zhang, Mississippi State Dept. of Health ; James Veillette, C Spire ; Kendell Churchwell, C Spire
12:15 PM	Floor Discussion

357	Tue, 8/1/2017, 10:30 AM - 11:15 AM	CC-Halls A&B
SPEED: Biopharmaceutical Statistics — Contributed Poster Presentations
Biopharmaceutical Section , Section on Statistical Learning and Data Science
Chair(s): Jessi Cisewski, Yale University
1:	Tradeoffs of a Randomize, Then Consent Approach to Improving Cluster Participation Rates in Cluster Randomize Trials — Abigail Shoben, The Ohio State University
2:	Sample Size for Joint Testing Cause-Specific Hazard and Overall Hazard in the Presence of Competing Risks — Qing Yang, Duke University ; Gang Li, University of California, Los Angeles
3:	Accounting for Baseline Covariates and Missing Data in Regulatory Trials with Longitudinal Designs — Elizabeth Colantuoni, Johns Hopkins University ; Jon Steingrimsson, Johns Hopkins University ; Aidan McDermott, Johns Hopkins University ; Michael Rosenblum, Johns Hopkins University
4:	Robust Feature Selection and Cell Line Classification with Electric Cell-Substrate Impedance Sensing Data — Megan Gelsinger, Cornell University ; David S Matteson, Cornell University ; Laurie Tupper, Williams College
5:	Comparing Biomarker-Guided Treatment Strategies Using Local Posterior Predictive Benefit — Meilin Huang, The University of Texas MD Anderson Cancer Center ; Brian P. Hobbs, The University of Texas MD Anderson Cancer Center
6:	A Comparison of Assay Platforms Using Correlation Coefficients in the Presence of Repeated Measurements — Qinlei Huang, Merck & Co. ; Radha Railkar, Merck & Co. ; Anita Lee, Merck & Co.
8:	On Measure of Surrogacy for Biomarkers in Medical Research. — Rui Zhuang, University of Washington ; Ying Qing Chen, Fred Hutchinson Cancer Research Center
9:	Discriminant Analysis (DA) Based Methods in Safety Evaluation — Angang Zhang, Merck ; Richard Baumgartner, Merck ; William W Wang, Merck & Co Inc
10:	On the Estimation of Risk Difference in the Presence of Continuous Baseline Covariates — Hua Ma, Merck ; Robin Mogg, Merck
11:	Power Comparison of Tests of Restricted Mean Survival Time with Log-Rank Test and Generalized Wilcoxon Test Under Various Survival Distributions — Musashi Fukuda, Astellas Pharma Inc. ; Yutaka Matsuyama, Department of Biostatistics, School of Public Health, The University of Tokyo
12:	Optimal Designs for Pharmacokinetic Studies Analyzed Using Non-Compartmental Methods — Helen Barnett, Lancaster University ; Thomas Jaki, Lancaster University ; Helena Geys, Janssen Pharmaceutica ; Tom Jacobs, Janssen Pharmaceutica
13:	Statistical Considerations in Delayed-Start Design to Demonstrate Disease Modification Effect in Neurodegenerative Disorders — Jun Zhao, AbbVie Inc. ; Deli Wang, AbbVie, Inc. ; Weining Z Robieson, AbbVie Inc.
14:	Placebo-Based Multiple Imputation Methods for Sensitivity Analysis in Recurrent Event Data — Rui Yang, Chiltern International Inc
15:	Sample Size Calculation for the Generalized Poisson Regression Model Comparing Two Rates of Recurrent Events — Kimitoshi Ikeda, Amgen Astellas BioPharma K.K.
16:	Sample Size Calculation to Support Local Submission — Zhuqing Yu, AbbVie ; Bidan Huang, AbbVie ; Jun Zhao, AbbVie Inc. ; lu cui, Abbvie
17:	An Examination of the Association Between Alcohol and Dementia in a Longitudinal Study — Tingting Hu, Florida State University ; Dan McGee, Florida State University ; Elizabeth Slate, Florida State University
18:	Design and Statistical Analysis of Method Transfer Studies for Biotechnology Products — Meiyu Shen, C DER, FDA ; Lixin Xu, FDA
The Speed portion will take place during Session 214526

359	Tue, 8/1/2017, 11:35 AM - 12:20 PM	CC-Halls A&B
SPEED: Computing, Graphics, and Programming Statistics — Contributed Poster Presentations
Section on Statistical Computing , Section on Statistical Graphics , Section for Statistical Programmers and Analysts , Quality and Productivity Section , Section on Statistical Consulting , Section on Statistical Learning and Data Science
Chair(s): Jessi Cisewski, Yale University
1:	Where Does All the Time Go? Measuring Clients' Service Utilization via Time-Tracking Software — Dou-Yan Yang, University of Wisconsin--Madison ; Jeff Havlena, University of Wisconsin--Madison
2:	Persistence Terrace for Topological Inference of Point Cloud Data — Chul Moon, University of Georgia ; Noah Giansiracusa, Swarthmore College ; Nicole Lazar, University of Georgia
4:	Generalized Bi-Plots for Visualized Multidimensional Projections — James Fry, Virginia Tech ; Matt Slifko, Virginia Tech ; Scotland Leman, Virginia Tech
5:	Dynamic Network Community Discovery — Shiwen Shen ; Edsel Aldea Pena, University of South Carolina
6:	Maintaining Trial Integrity for Randomized Open-Label Trials — Wenyun Ji, Amgen, Inc.
7:	A Mixture-Of-Regressions Model with Measurement Errors in the Response — Xiaoqiong Fang, University of Kentucky ; Derek Young, University of Kentucky
8:	A SAS Macro to Create Summary Tables — Amy Gravely, VA Medical Center ; Barbara Clothier, CCDOR MVAHCS
9:	Sequential Computer Experiments for Failure Probability Estimation --- a Floor System Example — Hao Chen, University of British Columbia ; William James Welch, University of British Columbia
11:	A Network-based Algorithm for Clustering Multivariate Longitudinal Data — Matthew Koslovsky, KBRwyle ; Millennia Young, National Aeronautics and Space Administration ; Caroline Schaefer, MEI Technologies ; John Arellano, MEI Technologies ; Al Feiveson, National Aeronautics and Space Administration
The Speed portion will take place during Session 214531

376 * !	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-331/332
Breiman Inaugural Award Lectures — Invited Papers
Section on Statistical Learning and Data Science
Organizer(s): Annie Qu, University of Illinois at Urbana-Champaign
Chair(s): Annie Qu, University of Illinois at Urbana-Champaign
2:05 PM	Adventures in Statistical Machine Learning — Grace Wahba, University of Wisconsin
2:35 PM	Statistical Learning for Individualized Treatment Rules — Yufeng Liu, University of North Carolina
3:05 PM	Statistical Inferences of Euclidean Distances — Ming Yuan, University of Wisconsin
12:05 PM	Floor Discussion

380	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-328
Recent Advances in High-Dimensional Statistics and Computational Methods — Invited Papers
IMS , Section on Statistical Learning and Data Science
Organizer(s): Yin Xia, Fudan University
Chair(s): Yin Xia, Fudan University
2:05 PM	SMART: Simultaneous Multistage Adaptive Ranking and Thresholding for Sparse Signal Recovery — Wenguang Sun, University of Southern California ; Weinan Wang, University of Southern California
2:30 PM	RESTRICTED STRONG CONVEXITY IMPLIES WEAK SUBMODULARITY — Sahand Negahban, Yale University ; Ethan Elenberg, UT Austin ; Rajiv Khanna, UT Austin ; Alex Dimakis, UT Austin
2:55 PM	Interactive Visualization and Fast Computation of the Solution Path for Convex Clustering and Biclustering — John Nagorski, Rice University ; Genevera I. Allen, Rice University
3:20 PM	Identifiability and Inference of Causal Effects with High-Dimensional and Invalid Instruments — Changjing Wu, Peking University ; Minghua Deng, Peking University ; Wei Lin, Peking University
3:45 PM	Floor Discussion

384 * !	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-337
Bayes, Frequentist, Fiducial: BFF in Action — Invited Papers
General Methodology , Section on Statistical Computing , Section on Statistical Learning and Data Science
Organizer(s): Nils Lid Hjort, University of Oslo
Chair(s): Nils Lid Hjort, University of Oslo
2:05 PM	Confidence Inference Function in Big Data — Peter XK Song, University of Michigan, ; Ling X.K. Zhou, University of Michigan
2:30 PM	Application of Generalized Fiducial Inference to Biological Sciences — Jan Hannig, University of North Carolina at Chapel Hill
2:55 PM	Combining Diverse Information Sources with the II-CC-FF Paradigm — Céline Cunen, University of Oslo ; Nils Lid Hjort, University of Oslo
3:20 PM	A Sequential Split-Conquer-Combine Approach for Gaussian Process Modeling in Computer Experiments — Ying Hung ; Minge Xie, Rutgers University ; Chengrui Li, Rutgers University
3:45 PM	Floor Discussion

385 * !	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-303
Machine Learning in Mental Health Research — Invited Papers
Mental Health Statistics Section , National Institute on Drug Abuse-NIH , Section on Statistical Learning and Data Science
Organizer(s): Booil Jo, Stanford University
Chair(s): Booil Jo, Stanford University
2:05 PM	Machine Learning Methods to Improve Causal Inference — Elizabeth Stuart, Johns Hopkins University
2:25 PM	Learning with Latent Trajectory Classes — Chen-Pin Wang, UTHSCSA ; Booil Jo, Stanford University
2:45 PM	Feature Construction for Automatic Detection of Stress and Anxiety — Donna Coffman, Temple University
3:05 PM	Discussant: Michael Freed, National Institute of Mental Health/NIH
3:25 PM	Discussant: Sarah Duffy, National Institute on Drug Abuse
3:45 PM	Floor Discussion

388 !	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-329
Random Matrices and Applications — Invited Papers
IMS , Section on Statistical Learning and Data Science
Organizer(s): Iain M Johnstone, Stanford Universty
Chair(s): Iain M Johnstone, Stanford Universty
2:05 PM	Free Component Analysis — Raj Rao Nadakuditi, University of Michigan
2:30 PM	High-Dimensional Cointegration Analysis — Alexei Onatski, University of Cambridge
2:55 PM	On Structure Testing for Component Covariance Matrices of a High-Dimensional Mixture — Jianfeng YAO, The University of Hong Kong ; Weiming Li, Shanghai University of Finance and Economics
3:20 PM	SHARP DETECTION in PCA UNDER CORRELATIONS — Edgar Dobriban, Stanford University
3:45 PM	Floor Discussion

391 * !	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-336
Statistical Process Control for Complex Data Structures — Topic Contributed Papers
Quality and Productivity Section , Section on Physical and Engineering Sciences , Section on Statistical Learning and Data Science
Organizer(s): Emmanuel Yashchin, IBM Research
Chair(s): Emmanuel Yashchin, IBM Research
2:05 PM	A Wavelet-Based Nonparametric CUSUM Control Chart for Autocorrelated Processes with Applications to Network Surveillance — Daniel Jeske, University of California, Riverside ; Jun Li, University of California, Riverside ; Yangmei Zhou, University of California, Riverside
2:25 PM	Prognostics and Health Management for Rechargeable Batteries — Kwokleung Tsui, City University of Hong Kong
2:45 PM	Social Media Monitoring: Monitoring Emotions Around the World to See What People React To — Ross Sparks, CSIRO
3:05 PM	Discussant: William H Woodall, Virginia Tech
3:25 PM	Floor Discussion

403	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-325
Selected Topics on Hypothesis Testing and Statistical Inference — Contributed Papers
IMS , Section on Statistics in Genomics and Genetics , Section on Statistical Learning and Data Science
Chair(s): Quefeng Li, The University of North Carolina at Chapel Hill
2:05 PM	A Neighborhood Hypothesis Test for Functional Data with an Application to Ecological Data — Leif Ellingson, Texas Tech University ; Dhanamalee Bandara, Texas Tech University ; Souparno Ghosh, Texas Tech University
2:20 PM	Testing for Model Adequacy in Censored Location-Scale Families — Sundarraman Subramanian, New Jersey Institute of Technology
2:35 PM	Nonparametric Inference via Bootstrapping the Debiased Estimator — Yen-Chi Chen, University of Washington
2:50 PM	A Higher Order Criticism of Higher Criticism — Thomas Porter, The University of Sydney ; Michael Stewart, The University of Sydney
3:05 PM	A Doubly Adaptive Inferential Method for Monotone Graph Invariants — Junwei Lu, Princeton University ; Matey Neykov, Princeton University ; Han Liu, Princeton University
3:20 PM	Hypothesis Testing for Simultaneous Variable Clustering and Correlation Network Estimation, with Application to Gene Co-Expression Networks — Kevin Lin, Carnegie Mellon University, Statistics Department ; Junwei Lu, Princeton University ; Han Liu, Princeton University ; Kathryn Roeder, Carnegie Mellon University
3:35 PM	Using Phylogenetic Models for Quantitative Trait Mapping with Multiple Loci — Katherine Thompson, University of Kentucky ; Catherine Linnen, University of Kentucky

415	Tue, 8/1/2017, 2:00 PM - 3:50 PM	CC-330
Methods for Functional or Network Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Ryan Warnick, Rice University
2:05 PM	Interaction and Model Selection for Function-On-Function Regression — Ruiyan Luo, Georgia State University ; Xin Qi, Georgia State University
2:20 PM	Simple Regression Model Using Shape Predictors — Kyungmin Ahn ; Wei Wu, Florida State University ; Anuj Srivastava, Florida State University
2:35 PM	Functional-On-Function Regression for Highly Densely Observed Spiky Functional Data — Xin Qi, Georgia State University ; Ruiyan Luo, Georgia State University
2:50 PM	Registration for Exponential Family Functional Data — Julia Wrobel, Columbia University, Department of Biostatistics ; Jeff Goldsmith, Columbia University
3:05 PM	Minimax Estimation for Varying Coefficient Model with Longitudinal Data — Xiaowu Dai, University of Wisconsin Madison
3:20 PM	Sensor-Based Localization of Diffusion Sources on Networks: a Bayesian Approach — Jun Li, Boston University ; Juliane Manitz, Boston University ; Enrico Bertuzzo, University Cà Foscari Venice ; Eric Kolaczyk, Boston University
3:35 PM	Floor Discussion

424	Tue, 8/1/2017, 3:05 PM - 3:50 PM	CC-Halls A&B
SPEED: Statistical Education — Contributed Poster Presentations
Section on Statistical Education , Section on Teaching of Statistics in the Health Sciences , Section on Statistical Learning and Data Science , Social Statistics Section
Chair(s): Jessi Cisewski, Yale University
1:	Benefits of Using Real Data Sets to Instruct Business Students in Data Mining Techniques — Kathleen Garwood, Saint Joseph's University
2:	A prediction model for understanding statistical replication — Andrew Neath, SIU Edwardsville
3:	Triathlon Road Closure Control — Zonghuan (Jason) Li, Student ; Mason Chen, Mason Chen Consulting
4:	Using an Alternative Sequence for Teaching an Undergraduate Introductory Statistics — Phyllis Curtiss, Grand Valley State University ; Robert Pearson, Grand Valley State University
5:	Reflections and Faculty Feedback on an Alternative Sequence for Teaching an Undergraduate Introductory Statistics Course — Robert Pearson, Grand Valley State University ; Phyllis Curtiss, Grand Valley State University
6:	Statistics as a Basic Leadership Competency: Making the Case to Executives and Educators — Matthew Jones, Walden University
7:	Learning Statistics with Productive Practice and Technology — Brenda Gunderson, Univ of Michigan
8:	Applying Logistic Regression to Student Data to Determine Retention of Students at a Large University — Reema Thakkar, RTI International ; Emily Griffith, NC State University ; Stephany Dunstan, N.C. State University
9:	First (?) Occurrence of the 25 Most Influential (?) Statistical Terms Introduced in the Last 20 Years — John McKenzie, Babson College
10:	Developing Partnerships with an AP Statistics Practice Exam — Christy Brown, Clemson University ; Ellen Breazel, Clemson University ; Elizabeth Johnson, George Mason University ; Jonathan Duggins, NC State University ; Bryan Crissinger, University of Delaware
11:	Helping Under Prepared Students Succeed in Introductory Statistics — Paul Plummer, University of Central Missouri
12:	Using Video Presentations for Assessment in Introductory Statistics Courses — Melissa Pittard, University of Kentucky
13:	Longitudinal Modeling in Applied Research: Implications for Improving Practice — Niloofar Ramezani, University of Northern Colorado ; Kerry Duck, University of Northern Colorado ; Austin Brown, University of Northern Colorado ; Michael Floren, University of Northern Colorado ; Krystal Hinerman, Lamar University ; Trent Lalonde, University of Northern Colorado
14:	P-Value as Strength of Evidence Measured by Confidence Distribution — Sifan Liu, Rutgers Univ Statistics Dept
15:	Definition and Confusion About Independence — Robert Molnar, Oklahoma State University
16:	If Only R Would Grade My Students' Projects — Robin Lock, St. Lawrence University
17:	Odds Ratio Versus Risk Ratio in Prevalence Trend Analysis — Scott McClintock, West Chester University ; Randall Rieger, West Chester University ; Zhen-qiang Ma, Pennsylvania Department of Health
18:	Using Data Mining to Identify At-Risk Freshmen — Nora Galambos, Stony Brook University
19:	Assessing the Effectiveness of Mentoring Youth — Laura Albrecht ; Keenan O'brien, Metropolitan State University of Denver ; Matthew Shaw, Metropolitan State University of Denver
The Speed portion will take place during Session 214536

425	Tue, 8/1/2017, 3:05 PM - 3:50 PM	CC-Halls A&B
SPEED: Reliable Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science , Section on Physical and Engineering Sciences , International Statistical Institute
Chair(s): Jessi Cisewski, Yale University
20:	Estimating an Inverse Mean Subspace — Jiaying Weng ; Xiangrong Yin, University of Kentucky
21:	Variable Selection on Functional Data Using Kernel Machine — Haoyu Wang, North Carolina State University
22:	Expected Conditional HSIC for Testing Independence — Chenlu Ke ; Xiangrong Yin, University of Kentucky
23:	Nonlinear Mixed-Effects Mixture Models for Clustering Longitudinal Data with an Application — Chongshu Chen, University of Rochester
24:	Coefficient Estimation and Hypothesis Testing on Interval-Valued Data Regression by Measurement Error — Yaotong Cai, University of Georgia ; lynne Billard, University of Georgia
25:	Quantifying Uncertainty in Latent Dirichlet Allocation — Christine Chai, Duke University
26:	Learning from Imbalanced Data: a Review of Some Existing Methodologies — Josephine Akosa, Oklahoma State University ; Melinda McCann, Oklahoma State University
27:	Tuning Variable Selection via Noise When Prediction Is Not the Primary Objective — Eric Reyes, Rose-Hulman Institute of Technology ; Xiaomo Wang, Rose-Hulman Institute of Technology
28:	A Simulation Study to Evaluate Variable Importance Measures in Random and Conditional Inference Forests with Imputation for Data with Missing and Correlated Predictors — Hung-Wen Yeh ; Rayus Kuplicki, Laureate Institute for Brain Research ; Trang Le, University of Tulsa ; Martin P. Paulus, Laureate Institute for Brain Research
29:	Sparse Network Tomography for Anomaly Detection — Elizabeth Hou, University of Michigan ; Yason Yilmaz, University of South Florida ; Alfred Hero, University of Michigan
30:	High-Dimensional Discriminant Analysis for Spatially Correlated Data — Yingjie Li, Michigan State University ; Tapabrata Maiti, Michigan State University
32:	Linear Combinations of Percentiles for Tests in the One Way Layout — Yvonne Zubovic, Indiana University Purdue University Fort Wayne
33:	Multivariate Functional Data Clustering with Automatic Variable Selection — Zhongnan Jin ; Yili Hong, Virginia Tech ; Qingyu Yang, Department of Industrial and Systems Engineering Wayne State University
34:	Robust Mode Detection Schemes in a Non-Stationary Environment — Francois Alastair Marshall, Queen's University
35:	Statistical Challenges in Materials Mechanics: Microstructure to Performance — Scott Vander Wiel, Los Alamos National Laboratory
36:	A New Multivariate Measure of Agreement in Method-Comparison Studies — Sasiprapa Hiriote, Department of Statistics, Faculty of Science, SILPAKORN UNIVERSITY ; Vernon M Chinchilli, Department of Public Health Sciences, Penn State Hershey College of Medicine
37:	Using Bandit Algorithms on Changing Reward Rates — Jeffrey Roach, OpenMail
38:	Supervised Binning Techniques for Predictive Modeling — Zhen Zhang, C Spire ; Lei Zhang, Mississippi State Dept. of Health ; James Veillette, C Spire ; Kendell Churchwell, C Spire
The Speed portion will take place during Session 214539

214728	Tue, 8/1/2017, 5:00 PM - 6:00 PM	H-Ruth
Section on Statistical Learning and Data Science Meeting — Other Cmte/Business
Section on Statistical Learning and Data Science
Chair(s): Annie Qu, University of Illinois at Urbana-Champaign

434	Wed, 8/2/2017, 7:00 AM - 8:15 AM
CANCELLED: Section on Statistical Learning and Data Science A.M. Roundtable Discussion (Added Fee) — Roundtables AM Roundtable Discussion
Section on Statistical Learning and Data Science

460 *	Wed, 8/2/2017, 8:30 AM - 10:20 AM	CC-322
Clustering Methods for Big Data Problems — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Ranjan Maitra, Iowa State University
Chair(s): Wei-Chen Chen, FDA/CDRH
8:35 AM	A Parallel EM Algorithm for Statistical Learning via Mixture Models — Geoffrey McLachlan, The University of Queensland
8:55 AM	Clustering Errored Sequence Reads to Estimate Unique Amplicons and Abundance — Karin Dorman, Iowa State University ; Xiyu Peng, Iowa State University
9:15 AM	A Bayesian Lasso Functional Clustering Model — Alejandro Murua, Universite de Montreal ; Folly Adjogou, Université de Montréal ; Wolfgang Raffelsberger, Institut de Génetique et de Biologie Moléculaire et Cellulaire, Université de Strasbourg
9:35 AM	Hierarchical Latent Factor Models for Improving the Prediction of Surgical Complications Across Hospitals — Elizabeth Lorenzi, Duke University ; Katherine Heller, Duke University ; Ricardo Henao, Duke University ; Zhifei Sun, Duke University
9:55 AM	Efficient Parallelized K-Means for Clustering Big Data — Geoffrey Thompson, Iowa State University ; Ranjan Maitra, Iowa State University
10:15 AM	Floor Discussion

461	Wed, 8/2/2017, 8:30 AM - 10:20 AM	CC-323
SPEED: Machine Learning — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): William Fithian, UC Berkeley Statistics
8:35 AM	Grapheme, Phoneme, Morpheme: Features for Text Classification — Taylor Arnold, University of Richmond
8:40 AM	Magic Cross-Validation with Applications in Kernel Smooth Margin Classifiers — Boxiang Wang ; Hui Zou, University of Minnesota
8:45 AM	Regression-Enhanced Random Forests — Haozhe Zhang, Iowa State University ; Dan Nettleton, Iowa State University ; Zhengyuan Zhu, Iowa State University
8:55 AM	Whiteout: Gaussian Adaptive Regularization Noise in Deep Neural Networks — Yinan Li, University of Notre Dame ; Ruoyi Xu, University of Science and Technology of China ; Fang Liu, University of Notre Dame
9:00 AM	Support Vector Machine with Confidence — Haomiao Meng, Binghamton University ; Wenbo Wang, Binghamton University ; Xingye Qiao, Binghamton University
9:05 AM	Bernstein and Hoeffding Type Inequalities for Regenerative Markov Chains — Gabriela Cio?ek, Telecom ParisTech ; Patrice Bertail, Université Paris Ouest Nanterre
9:15 AM	High Dimensional Bayesian Optimization with both Continuous and Categorical Explanatory Variables — Nima Dolatnia ; Alan Fern, Oregon State University ; Sarah Emerson, Oregon State University
9:20 AM	A Robust Residual-Based Approach for Random Forest Regression — Andrew Sage, Iowa State University ; Ulrike Genschel, Iowa State University ; Dan Nettleton, Iowa State University
9:30 AM	Estimation and Model Selection by Data-Driven Weighted Likelihoods — Sam-Erik Walker, Dept. of Math., Univ. of Oslo ; Nils Lid Hjort, University of Oslo
9:35 AM	Adaptive Pruning for Random Forests: It Helps — Thomas Loughin, Simon Fraser University ; Andrew Henrey, Simon Fraser University
9:40 AM	Tree-Based Models for Longitudinal Data — Brittany Green, University of Cincinnati ; Peng Wang, University of Cincinnati
9:45 AM	Group Fused Multinomial Regression — Brad Price, West Virginia University ; Adam Rothman, University of Minnesota ; Charles Geyer, University of Minnesota
9:50 AM	Statistical Significance of Clustering — Purvasha Chakravarti, Carnegie Mellon University ; Larry Wasserman, Carnegie Mellon ; Sivaraman Balakrishnan, Department of Statistics, CMU
9:55 AM	Joint Sentiment Topic Modeling of Text Data — Sahba Akhavan Niaki, University of Florida ; George Michailidis, University of Florida
10:00 AM	Building Comprehensive Searches Through a Machine Learning Approach for Systematic Reviews — Corrado Lanera, University of Padova ; Ileana Baldi, University of Padova ; Clara Minto, University of Padova ; Dario Gregori, University of Padova ; Paola Berchialla, University of Torino
10:05 AM	The Geometry of Nonlinear Embeddings in Discriminant Analysis with Gaussian Kernels — Jiae Kim, Ohio State University ; Yoonkyung Lee, The Ohio State University
10:10 AM	Floor Discussion

465 *	Wed, 8/2/2017, 8:30 AM - 10:20 AM	CC-313
Biometrics and High-Dimensional Data — Contributed Papers
Biometrics Section , Section on Statistics in Genomics and Genetics , Section on Statistical Learning and Data Science
Chair(s): Huaqing Zhao
8:35 AM	Data-Adaptive Statistics for Multiple Hypothesis Testing in High-Dimensional Settings — Weixin Cai, University of California, Berkeley ; Alan Hubbard, University of California, Berkeley
9:05 AM	Grouping Methods for Estimating the Prevalences of Rare Traits from Complex Survey Data that Preserve Confidentiality of Respondents — Noorie Hyun, National Cancer Institute ; Joseph Gastwirth, the George Washington University ; Barry I. Graubard, National Cancer Institute
9:20 AM	Test for a Change Point in the Linear Combination of Risk Predictors — Ju Hee Cho, Fred Hutchinson Cancer Research Center ; Ying Huang, Fred Hutchinson Cancer Research Center
9:35 AM	Score Test for Case-Cohort Design Applied to High-Throughput Gene Expression Analysis — Huining Kang, University of New Mexico ; John Carl Pesko, University of New Mexico
9:50 AM	Identification of Conditionally Essential Genes in Transposon Sequencing Studies — Lili Zhao, University of Michigan
10:05 AM	Empirical Null Estimation Using Zero Inflated Discrete Mixture Distributions and Its Application to Protein Domain Data — Iris Ivy Gauran ; Junyong Park, University of Maryland at Baltimore County ; DoHwan Park, University of Maryland, Baltimore County ; Maricel Kann, University of Maryland, Baltimore County ; Thomas Peterson, University of Maryland, Baltimore County ; John Zylstra, University of Maryland, Baltimore County ; John Spouge, NCBI,NLM,NIH ; Johan Lim, Seoul National University

477 *	Wed, 8/2/2017, 10:30 AM - 12:20 PM	CC-336
Bayesian Methods for High-Dimensional Inference — Invited Papers
IMS , Section on Bayesian Statistical Science , International Society for Bayesian Analysis (ISBA) , Section on Statistical Learning and Data Science
Organizer(s): David Dunson, Duke University
Chair(s): David Dunson, Duke University
10:35 AM	Clustering High-Dimensional Data Using Summary Statistics — Valen Johnson, Texas A&M University
11:00 AM	Bayesian Partition Logistic Regression Models — Jun Liu, Harvard University
11:25 AM	High-Dimensional Linear Regression via the R-Squared Induced Dirichlet Decomposition Prior — Howard Bondell, NC State University ; Brian Reich, NCSU ; Yan Dora Zhang, Johns Hopkins
11:50 AM	Nonparametric Maximum Likelihood Approximate Message Passing — Lee Dicker, Rutgers University ; Ruijun Ma, Rutgers University
12:15 PM	Floor Discussion

478	Wed, 8/2/2017, 10:30 AM - 12:20 PM	CC-330
Online Machine Learning for Prediction and Sequential Decision Making — Invited Papers
IMS , Association for the Advancement of Artificial Intelligence , Institute for Operations Research and the Management Sciences , Section on Statistical Learning and Data Science
Organizer(s): Susan A Murphy, University of Michigan
Chair(s): Susan A Murphy, University of Michigan
10:35 AM	A New Approach to Online Prediction — Alexander Rakhlin, University of Pennsylvania
11:00 AM	Online Decision-Making with High-Dimensional Covariates — Hamsa Bastani, Stanford University ; Mohsen Bayati, Stanford University
11:25 AM	Regret Bounds for Adaptive Control of Linear Quadratic Systems — Mohamad Kazem Shirani Faradonbeh, University of Michigan ; Ambuj Tewari, University of Michigan ; George Michailidis, University of Florida
11:50 AM	Improved Strongly Adaptive Online Learning Using Coin Betting — Rebecca Willett, University of Wisconsin-Madison ; Kwang-Sung Jun, University of Wisconsin-Madison ; Francesco Orabona, Stony Brook University ; Stephen Wright, University of Wisconsin-Madison
12:15 PM	Floor Discussion

483	Wed, 8/2/2017, 10:30 AM - 12:20 PM	CC-350
Integration of Diverse Data Sets — Invited Papers
Section on Statistical Learning and Data Science , Section for Statistical Programmers and Analysts , Section on Statistics in Genomics and Genetics
Organizer(s): Michelle Schwalbe, National Academies, CATS
Chair(s): Michael J Daniels, University of Texas at Austin
10:35 AM	Statistical Data Integration Through Networks — Genevera I. Allen, Rice University
11:00 AM	Data Integration for Heterogenous Data Sets — Alfred Hero, III, University of Michigan
11:25 AM	Learning from Multiple Views of a Single Set of Observations — Daniela Witten, University of Washington
11:50 AM	Discussant: Jeffrey S. Morris, The University of Texas M.D. Anderson Cancer Center
12:10 PM	Floor Discussion

494 * !	Wed, 8/2/2017, 10:30 AM - 12:20 PM	CC-311
Statistical Methodologies for Identifying, Modeling, and Managing Subpopulations at Risk — Topic Contributed Papers
Section on Statistics in Epidemiology , Health Policy Statistics Section , Section on Statistical Learning and Data Science
Organizer(s): howard burkom, Johns Hopkins University Applied Physics Laboratory
Chair(s): Suchi Saria, Johns Hopkins University
10:35 AM	Data Analysis Techniques for Large and Changing Populations — Jason Lee, Johns Hopkins University Applied Physics Laboratory
10:55 AM	Machine Learning Methods in the Statistical Prediction of Health Outcomes — William Padula, Johns Hopkins Bloomberg SPH
11:15 AM	A Comparison of Risk Adjustment Models Based on Traditional Statistical and Machine Learning Techniques — Hong Kan ; Hsien-Yen Chang, Johns Hopkins Bloomberg School of Public Health ; Hadi Kharrazi, Johns Hopkins Bloomberg School of Public Health
11:35 AM	Combining Multiple Sources of Public Health Surveillance Information with Analytic Methods — howard burkom, Johns Hopkins University Applied Physics Laboratory
11:55 AM	Discussant: Hadi Kharrazi, Center for Population Health IT, Johns Hopkins Bloomberg School of Public Health
12:15 PM	Floor Discussion

512	Wed, 8/2/2017, 10:30 AM - 12:20 PM	CC-323
Bayesian Model Selection — Contributed Papers
Section on Bayesian Statistical Science , Section on Statistical Learning and Data Science
Chair(s): Chris Saunders
10:35 AM	Inflated Values Selection Method on Multiple-Inflated Poisson Model — Qiuya Li ; Kwok Fai Geoffrey TSO, City University of Hong Kong ; Yichen Qin, University of Cincinnati ; Yang Li, Renmin University of China
10:50 AM	Posterior Graph Selection and Estimation Consistency for High-Dimensional Bayesian DAG Models — Xuan Cao, University of Florida ; Kshitij Khare, University of Florida ; Malay Ghosh, University of Florida
11:05 AM	Fast and Accurate Bayesian Model Criticism and Conflict Detection Using Integrated Nested Laplace Approximations (INLA) — Egil Ferkingstad, University of Iceland ; Leonhard Held, University of Zurich ; Havard Rue, KAUST
11:20 AM	Bayesian High-Dimensional Linear Regression Under the Model Misspecification — Kyoungjae Lee, The University of Notre Dame ; Minwoo Chae, The University of Texas at Austin ; Lizhen Lin, The University of Notre Dame
11:35 AM	Bayesian Model Selection in the Presence of Heteroscedasticity in Linear Models — Thomas Metzger, Virginia Tech ; Christopher T. Franck, Virginia Tech Department of Statistics
11:50 AM	A Study of Delay Discounting Using Bayesian Model Selection — Christopher T. Franck, Virginia Tech Department of Statistics
12:05 PM	Bayesian Regression with Proximal Algorithm — Lei Sun, University of Chicago ; Nicholas Polson, University of Chicago

519	Wed, 8/2/2017, 10:30 AM - 12:20 PM	CC-348
Sparse Statistical Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Sumanta Basu, Cornell University
10:35 AM	Rare Feature Selection in High Dimensions — Xiaohan Yan, Cornell University ; Jacob Bien, Cornell University
10:50 AM	Decorrelation of Covariates for High-Dimensional Sparse Regression — Yuan Ke, Princeton University ; Jianqing Fan, Princeton University ; Kaizheng Wang, Princeton University
11:05 AM	Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization — Xingguo Li, University of Minnesota ; Jarvis Haupt, University of Minnesota ; Zhaoran Wang, Princeton University ; Junwei Lu, Princeton University ; Han Liu, Princeton University ; Raman Arora, Johns Hopkins University ; Tuo Zhao, Georgia Institute of Technology
11:20 AM	Properties of Data-Dependent Variable Selection Methods — Sen Tian, New York University, Stern School of Business ; Clifford M. Hurvich, New York University, Stern School of Business ; Jeffrey S. Simonoff, New York University, Stern School of Business
11:35 AM	Variable Selection for Massive Data Using the Divide And — Lei Yang, New York University ; Yixin Fang, New Jersey Institute of Technology ; Junhui Wang, CityU ; Yongzhao Shao, New York University-School of Medicine
11:50 AM	Efficient Bounds for Best Subset Selection Using Mixed Integer Linear Programming — Ana Kenney ; Francesca Chiaromonte, Penn State University ; Giovanni Felici, IIASI CNR
12:05 PM	Variable Selection for a Mixture of Linear Mixed Effects Models — Yian Zhang, New York University ; Lei Yang, New York University ; Yongzhao Shao, New York University-School of Medicine

529	Wed, 8/2/2017, 10:30 AM - 11:15 AM	CC-Halls A&B
SPEED: Machine Learning — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Jessi Cisewski, Yale University
1:	Grapheme, Phoneme, Morpheme: Features for Text Classification — Taylor Arnold, University of Richmond
2:	Magic Cross-Validation with Applications in Kernel Smooth Margin Classifiers — Boxiang Wang ; Hui Zou, University of Minnesota
3:	Regression-Enhanced Random Forests — Haozhe Zhang, Iowa State University ; Dan Nettleton, Iowa State University ; Zhengyuan Zhu, Iowa State University
5:	Whiteout: Gaussian Adaptive Regularization Noise in Deep Neural Networks — Yinan Li, University of Notre Dame ; Ruoyi Xu, University of Science and Technology of China ; Fang Liu, University of Notre Dame
6:	Support Vector Machine with Confidence — Haomiao Meng, Binghamton University ; Wenbo Wang, Binghamton University ; Xingye Qiao, Binghamton University
7:	Bernstein and Hoeffding Type Inequalities for Regenerative Markov Chains — Gabriela Cio?ek, Telecom ParisTech ; Patrice Bertail, Université Paris Ouest Nanterre
9:	High Dimensional Bayesian Optimization with both Continuous and Categorical Explanatory Variables — Nima Dolatnia ; Alan Fern, Oregon State University ; Sarah Emerson, Oregon State University
10:	A Robust Residual-Based Approach for Random Forest Regression — Andrew Sage, Iowa State University ; Ulrike Genschel, Iowa State University ; Dan Nettleton, Iowa State University
11:	Estimation and Model Selection by Data-Driven Weighted Likelihoods — Sam-Erik Walker, Dept. of Math., Univ. of Oslo ; Nils Lid Hjort, University of Oslo
12:	Adaptive Pruning for Random Forests: It Helps — Thomas Loughin, Simon Fraser University ; Andrew Henrey, Simon Fraser University
13:	Tree-Based Models for Longitudinal Data — Brittany Green, University of Cincinnati ; Peng Wang, University of Cincinnati
14:	Group Fused Multinomial Regression — Brad Price, West Virginia University ; Adam Rothman, University of Minnesota ; Charles Geyer, University of Minnesota
15:	Statistical Significance of Clustering — Purvasha Chakravarti, Carnegie Mellon University ; Larry Wasserman, Carnegie Mellon ; Sivaraman Balakrishnan, Department of Statistics, CMU
16:	Joint Sentiment Topic Modeling of Text Data — Sahba Akhavan Niaki, University of Florida ; George Michailidis, University of Florida
17:	Building Comprehensive Searches Through a Machine Learning Approach for Systematic Reviews — Corrado Lanera, University of Padova ; Ileana Baldi, University of Padova ; Clara Minto, University of Padova ; Dario Gregori, University of Padova ; Paola Berchialla, University of Torino
18:	The Geometry of Nonlinear Embeddings in Discriminant Analysis with Gaussian Kernels — Jiae Kim, Ohio State University ; Yoonkyung Lee, The Ohio State University
The Speed portion will take place during Session 214518

549 * !	Wed, 8/2/2017, 2:00 PM - 3:50 PM	CC-309
Recent Development of Statistical Learning Methods for Complex Biomedical Data — Invited Papers
ENAR , Biometrics Section , Section on Statistical Learning and Data Science , Section on Statistics in Imaging
Organizer(s): Wenbin Lu, North Carolina State University
Chair(s): Wenbin Lu, North Carolina State University
2:05 PM	Maximum likelihood inference for a large precision matrix — Yunzhang Zhu, Ohio State University ; Xiaotong T Shen, University of Minnesota ; Wei Pan, University of Minnesota
2:30 PM	Statistical Modeling of Large-Scale Neural Ensembles — Shizhe Chen, Columbia University ; Ali Shojaie, University of Washington ; Eric Shea-Brown, University of Washington ; Daniela Witten, University of Washington
2:55 PM	Optimal Individualized Treatment Strategy with Imaging Covariates — Rui Song, NC State University
3:20 PM	Efficient Use of EHR for Comparative Effectiveness Research — Tianxi Cai, Harvard University
3:45 PM	Floor Discussion

558 * !	Wed, 8/2/2017, 2:00 PM - 3:50 PM	CC-329
Recent Developments in Statistics of Economic Data in High-Dimensional Contexts — Invited Papers
Business and Economic Statistics Section , JBES-Journal of Business & Economic Statistics , Social Statistics Section , Section on Statistical Learning and Data Science
Organizer(s): Arnab Bhattacharjee, Heriot-Watt University
Chair(s): Soutir Bandyopadhyay, Lehigh Univeristy
2:05 PM	A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices — Natalia Bailey, Monash University ; Hashem Pesaran, University of Southern California and Trinity College, Cambridge ; Vanessa Smith, University of York
2:35 PM	Post-Model Selection Estimation for Regression Models with Spatial Autoregressive Errors — Tapabrata Maiti, Michigan State University ; Liqian Cai, Liberty Mutual
3:05 PM	Structural Economic Models with Large Number of Potential Explanatory Variables and Inclusion/Exclusion Restrictions — Arnab Bhattacharjee, Heriot-Watt University ; Geoffrey Hewings, University of Illinois
3:35 PM	Floor Discussion

588	Wed, 8/2/2017, 2:00 PM - 3:50 PM	CC-337
Statistical Learning: Clustering — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): John Nagorski, Rice University
2:05 PM	Randomized SUP: a Clustering Algorithm for Large-Scale Data — Shang-Ying Shiu, Department of Statistics, National Taipei University ; Ting-Li Chen, Institute of Statistical Sciences, Academia Sinica ; Yen-Shiu Chin, Institute of Statistical Sciences, Academia Sinica ; Wush Wu, Department of Electrical Engineering, National Taiwan University
2:20 PM	Multi Level Clustering Technique Leveraging Expert Insight — Sudhanshu Singh, IBM India Pvt. ltd. ; Ritwik Chaudhuri, IBM India Pvt. ltd. ; Manu Kuchhal, IBM India Pvt. ltd. ; Sarthak Ahuja, IBM India Pvt. ltd. ; Gyana Parija, IBM India Pvt. ltd.
2:35 PM	Variable Selection in K-Means Clustering — Nicholas Scott Berry, Iowa State University ; Ranjan Maitra, Iowa State University
2:50 PM	Two-Layer Heterogeneity Model for Massive Data — Ching-Wei Cheng, Purdue University ; Guang Cheng, Purdue
3:05 PM	Topological Probabilistic Classification (TopProC) — Fairul Mohd-Zaid, Air Force Research Lab ; Christine Schubert Kabban, Air Force Institute of Technology
3:20 PM	Poisson-Kernel Based Clustering on the Sphere: Convergence Properties, Initialization Rules and a Method of Sampling — Mojgan Golzy, State University of New York At Buffalo ; Marianthi Markatou, University at buffalo, Department of Biostatistics ; Alexander Foss, State University of New York at Buffalo
3:35 PM	A One-Class Convex Hull Peeling Method for Outlier Detection — Waldyn Martinez, Miami University ; Maria L. Weese, Miami University ; Allison Jones-Farmer, Miami University

596 * !	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-315
Novel Statistical Approaches to Essential Cybersecurity Problems — Invited Papers
Section on Statistics in Defense and National Security , Section on Statistical Learning and Data Science , Government Statistics Section
Organizer(s): Natallia V Katenka, University of Rhode Island
Chair(s): Gavino Puggioni, University of Rhode Island
8:35 AM	Statistical Anomaly Detection Framework for Cyber-Security — Marina Evangelou, Imperial College London ; Niall Adams, Imperial College
9:05 AM	Discussant: Natallia V Katenka, University of Rhode Island
9:35 AM	Discussant: Andrey Lokhov, Theoretical Division, Los Alamos National Laboratory
10:05 AM	Floor Discussion

599 * !	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-323
Creating Interactive Graphics — Invited Papers
Section on Statistical Graphics , Section on Statistical Learning and Data Science , Section for Statistical Programmers and Analysts , Conference on Statistical Practice Steering Committee
Organizer(s): John Muschelli, Johns Hopkins University
Chair(s): Peter Hickey, Johns Hopkins University
8:35 AM	Interactive Data Visualization on the Web Using R — Carson Sievert, Carson Sievert
9:00 AM	Guiding Principles for Interactive Graphics Based on LIBD Data Science Projects — Leonardo Collado-Torres, Lieber Institute for Brain Development ; Bill Ulrich, Lieber Institute for Brain Development ; Stephen Semick, Lieber Institute for Brain Development ; Andrew E Jaffe, Lieber Institute for Brain Development
9:25 AM	Beyond Axes: Simulating Systems with Interactive Graphics — Sean Kross, Johns Hopkins University
9:50 AM	Discussant: John Muschelli, Johns Hopkins University
10:05 AM	Floor Discussion

600 * !	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-340
High-Dimensional Time Series and Applications in Social and Biological Sciences — Invited Papers
WNAR , Section on Statistical Computing , Section on Statistical Learning and Data Science
Organizer(s): Ali Shojaie, University of Washington
Chair(s): Ali Shojaie, University of Washington
8:35 AM	A Flexible Non-parametric Framework for High-Dimensional Multi-way Data — Zhaoxia Yu, University of California, Irvine ; Tong Shen, University of California, Irvine ; Dustin Pluta, University of California, Irvine ; Hernando Ombao, University of California, Irvine; King Abdullah University of Science and Technology
9:00 AM	Granger Causality Networks for Categorical Time Series: Convex Mixture Transition Distribution — Alex Tank, University of Washington ; Ali Shojaie, University of Washington ; Emily Fox, University of Washington
9:25 AM	Leaning Multivariate High-Dimensional Point Process Models — Garvesh Raskutti, University of Wisconsin-Madison
9:50 AM	Eigenvalues of Covariance Matrices of High-Dimensional Time Series — Alexander Aue, University of California, Davis ; Haoyang Liu, University of California, Berkeley ; Debashis Paul, University of California, Davis
10:15 AM	Floor Discussion

602 * !	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-321
Bridging the Gap Between Statistics and Other Data Sciences: Where's the Bridge? Where's the Gap? — Invited Panel
Section on Statistical Consulting , Section on Statistical Learning and Data Science , Section on Statistical Computing , Statistics Without Borders , Conference on Statistical Practice Steering Committee , Statistics in Business Schools Interest Group
Organizer(s): Jason Brinkley, American Institutes for Research
Chair(s): Jason Brinkley, American Institutes for Research
8:35 AM	Bridging the Gap Between Statistics and Other Data Sciences: Where's the Bridge? Where's the Gap?
Panelists:	Edward Boone, Virginia Commonwealth University Randy Bartlett, Blue Sigma Analytics Mark Lancaster, Northern Kentucky University Andrew Ekstrom, University of Michigan - Dearborn Fred Hulting, General Mills
10:10 AM	Floor Discussion

609 * !	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-303
New Advances in Analysis of Complex Cohort Studies — Topic Contributed Papers
Korean International Statistical Society , Section on Statistical Learning and Data Science
Organizer(s): Hyokyoung (Grace) Hong, Michigan State University
Chair(s): Jae-kwang Kim, Iowa State University
8:35 AM	Latent Class Analysis for Modeling and Promoting Online Learning — Jeff Douglas ; Shiyu Wang, University of Georgia ; Steven Culpepper, University of Illinois
8:55 AM	Variable Selection for Case-Cohort Studies with Failure Time Outcome — Jianwen Cai, University of North Carolina at Chapel Hill ; Ai Ni, Memorial Sloan Kettering Cancer Center ; Donglin Zeng, University of North Carolina
9:15 AM	Integrated Powered Density: Screening Utrahigh-Dimensional Covariates with Survival Outcomes — Hyokyoung (Grace) Hong, Michigan State University ; Yi Li, University of Michigan ; Xuerong Chen, Southwest University of Finance and Economics
9:35 AM	Fixed Support Positive-Definite Modification of Covariance Matrix Estimators via Linear Shrinkage — Johan Lim, Seoul National University ; Young-Geun Choi, Fred Hutchinson Cancer Research Center ; Junyong Park, University of Maryland at Baltimore County ; Anindya Roy, University of Maryland at Baltimore County
9:55 AM	Nonparametric Conditional Graphical Models with High-Dimensional Predictors — Yi Li, University of Michigan
10:15 AM	Floor Discussion

611 !	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-314
Recent Advances in High-Dimensional Statistical Inference — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Tingni Sun, University of Maryland
Chair(s): Xialu Liu, San Diego State University
8:35 AM	On the Estimation of Ultra-High-Dimensional Semiparametric Gaussian Copula Models — Qing Mai
8:55 AM	Statistical Inference in Large Ising Graphical Models via Quadratic Programming — Zhao Ren, University of Pittsburg ; Cun-Hui Zhang, Rutgers University ; Harrison H. Zhou, Yale University ; Sai Li, Rutgers University
9:15 AM	A Likelihood Ratio Based Goodness of Fit Test for Graphical Models — Ritwik Mitra
9:35 AM	Adaptive p-values after cross-validation — Lucy Xia ; Jonathan Taylor, Stanford University ; Jelena Markovic, Stanford University
9:55 AM	New Insights About High-Dimensional Statistical Inference — Lingzhou Xue, The Pennsylvania State University ; Danning Li, Jilin University
10:15 AM	Floor Discussion

612 * !	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-306
New Challenges in High-Dimensional Statistical Inference — Topic Contributed Papers
IMS , Section on Statistics in Genomics and Genetics , Section on Statistical Learning and Data Science
Organizer(s): Lingzhou Xue, The Pennsylvania State University
Chair(s): Zheng Ke, University of Chicago
8:35 AM	Optimal Estimation of Co-Heritability in High-Dimensional Linear Models — Zijian Guo, University of Pennsylvania, Wharton School ; Wangjie Wang , National University of Singapore ; Tony Cai, University of Pennsylvania ; Hongzhe Li, University of Pennsylvania
8:55 AM	Some New Insights in High-Dimensional Independence Tests — Danning Li, Jilin University
9:15 AM	Pairwise Difference Estimation of High Dimensional Partially Linear Model — Fang Han, University of Washington ; Zhao Ren, University of Pittsburg ; Yuxin Zhu, Johns Hopkins University
9:35 AM	Homogeneity Test of Covariance Matrices with High-Dimensional Longitudinal Data — Pingshou Zhong, Michigan State University ; Runze Li, The Pennsylvania State University
9:55 AM	Floor Discussion

630 *	Thu, 8/3/2017, 8:30 AM - 10:20 AM	CC-313
Machine Learning Applications — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Fei Xue, University of Illinois at Urbana-Champaign
8:35 AM	Quantifying the Influence of Tourism on 19th Century American Landscape Painting Using Galois Lattices of Affiliation Networks — Roger Bilisoly, Central Connecticut State University
8:50 AM	Cyber-Security: a Stochastic Predictive Model to Determine Overall Network Security Risk Using Markovian Process — Nawa Raj Pokhrel
9:05 AM	Beats, Rhymes, Life, and Text Analysis: Analyzing the Lyrics of a Tribe Called Quest — Clayton Barker, SAS Institute
9:20 AM	Statistial Learning in Gender Classification for Facial Images — Cuixian Chen, University of North Carolina, Wilmington ; Yishi Wang, University of North Carolina Wilmington
9:35 AM	Comparison of Variable Selection Methods on a National Survey of Drug Use and Health — Georgiy Bobashev, RTI International ; Li-Tzy Wu, Duke University
9:50 AM	A Data Science Approach to Analyzing Neural Data — Ethan Meyers
10:05 AM	Comparison and validation of statistical methods for predicting tree failure during storm — Elnaz Kabir ; Seth Guikema, University of Michigan

633 * !	Thu, 8/3/2017, 10:30 AM - 12:20 PM	CC-331/332
Analysis of Complex Random Networks on the Guard of National Security — Invited Papers
SSC , Section on Statistics in Defense and National Security , Section on Statistical Learning and Data Science
Organizer(s): Yulia R. Gel, University of Texas at Dallas, Vyacheslav Lyubchich, University of Maryland Center for Environmental Science
Chair(s): Vyacheslav Lyubchich, University of Maryland Center for Environmental Science
10:35 AM	Dynamic Text Networks: An Application to Political Blogs — David Banks, Duke University
11:05 AM	Inference and Analysis on Social Networks from Newswire Content — William Campbell, MIT Lincoln Laboratory ; Lin Li, MIT Lincoln Laboratory ; Joel Acevedo-Aviles, MIT Lincoln Laboratory
11:35 AM	Supervised Community Detection in Dark Networks — Yulia R. Gel, University of Texas at Dallas ; Yahui Tian, University of Texas at Dallas
12:05 PM	Floor Discussion

635 * !	Thu, 8/3/2017, 10:30 AM - 12:20 PM	CC-316
Advanced Machine Learning Methods for Large-Scale Imaging Data — Invited Papers
Section on Statistical Learning and Data Science , Section on Statistics in Imaging , Section on Statistics in Genomics and Genetics
Organizer(s): Xuan Bi, Yale University
Chair(s): Xiwei Tang, University of Illinois at Urbana-Champaign
10:35 AM	A Statistical Method for Advancing Neuroimaging Genetics — Xuan Bi, Yale University ; Hongtu Zhu, The University of Texas MD Anderson Cancer Center ; Heping Zhang, Yale University School of Public Health
11:00 AM	Bayesian Inference for the Directional Brain Network Modeled by High-Dimensional Damped Harmonic Oscillators Using ECoG Data — Tingting Zhang, University of Virginia ; Qiannan Yin, University of Virginia ; Yinge Sun, University of Virginia ; Brian Scott Caffo, Johns Hopkins ; Dana Boatman-Reich, Johns Hopkins University
11:25 AM	Convex clustering over an undirected graph — Yunzhang Zhu, Ohio State University ; Vincent Vu, The Ohio State University
11:50 AM	Adaptive Testing of SNP-Brain Functional Connectivity Associations Using Modular Network Structures — Wei Pan, University of Minnesota
12:15 PM	Floor Discussion

646 *	Thu, 8/3/2017, 10:30 AM - 12:20 PM	CC-317
Big Data with Bite-Sized Solutions — Topic Contributed Papers
Section for Statistical Programmers and Analysts , Biopharmaceutical Section , Section on Statistical Learning and Data Science , Conference on Statistical Practice Steering Committee
Organizer(s): Vipin Arora, Eli Lilly and Company
Chair(s): Ram Tiwari, FDA/CDER/OT/OB
10:35 AM	Big Data with Bite Size Solutions — Spencer Lourens, Indiana University
10:55 AM	Piecewise Solutions to Big Data — Anuradha Roy, The University of Texas at San Antonio ; Henry Chacon, The University of Texas at San Antonio
11:15 AM	Leveraging Machine Learning in the Analysis of Safety Data in Drug Research and Healthcare Informatics — Melvin Munsaka, Safety Statistics and Observational Res Analytics, Takeda
11:35 AM	Uncovering What Is Inside the Data Using Effective Visualization — Vipin Arora, Eli Lilly and Company ; Xiang Zhang, Eli Lilly and Company
11:55 AM	Discussant: Judith D. Goldberg, New York Unversity School of Medicine
12:15 PM	Floor Discussion

JSM 2017 Online Program

Activity Details

ASA Meetings Department

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