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
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
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
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