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Sessions Were Renumbered as of May 19.

Legend:
CC-W = McCormick Place Convention Center, West Building,   CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago,   UC= Conference Chicago at University Center
* = applied session       ! = JSM meeting theme

Activity Details


Register CE_10C
Sun, 7/31/2016, 8:30 AM - 5:00 PM CC-W470a
Introduction to Statistical Learning for Unsupervised Problems (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Statistical Learning and Data Science
Instructor(s): Ali Shojaie, University of Washington
8:30 AM Introduction to Statistical Learning for Unsupervised Problems (ADDED FEE) Ali Shojaie, University of Washington
 
 

20 !
Sun, 7/31/2016, 2:00 PM - 3:50 PM CC-W175c
Ranking Methods: Infinite Permutations, Statistical Physics, Copulas, and Seriation for Discovery of Subpopulations in Big Data — Topic Contributed Papers
Section on Statistical Learning and Data Science , IMS
Organizer(s): Joseph S. Verducci, The Ohio State University
Chair(s): Stephen Bamattre, Ensemble Lending
2:05 PM Viewing a Permutation as a Measure on the Unit Square Sumit Mukherjee, Columbia University
2:25 PM The Large N Limit of the Mallows Model Shannon Starr, University of Alabama at Birmingham ; Meg L. Walters, University of Rochester
2:45 PM A Multiparameter Mallows Model for Infinite Rankings Marina Meila, University of Washington
3:05 PM Top-K Tau-Path Subpopulation Screen for Monotone Association Joseph S. Verducci, The Ohio State University ; Srinath Sampath, Hamilton Capital Management ; Adriano Caloiaro, Myatt and Johnson ; Wayne Johnson, Myatt and Johnson
3:25 PM A Distribution Function Approach for Signal Reconstruction from Ranking Data Michael Schimek, Medical University of Graz ; Vendula Svendova, Medical University of Graz
3:45 PM Floor Discussion
 
 

36
Sun, 7/31/2016, 2:00 PM - 3:50 PM CC-W176a
Sufficient Dimension Reduction and Projection Methods — Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Chair(s): Glen Wright Colopy, University of Oxford
2:05 PM Selective Combinations of Central Matrices for Dimension Reduction Son Nguyen
2:20 PM On Sufficient Dimension Reduction for Functional Data Jun Song, Penn State University ; Bing Li, Penn State University
2:35 PM Determining the Number of Components in a Generalized Spiked Population Model Hyo Young Choi
2:50 PM On Likelihood Ratio Tests in Dimensionality-Restricted Models Mingyue Gao ; Michael Trosset, Indiana University ; Carey Priebe, The Johns Hopkins University
3:05 PM Partial Projective Resampling Method for Dimension Reduction: With Applications to Partially Linear Models Haileab Hilafu, University of Tennessee ; Wenbo Wu, University of Oregon
3:20 PM On Estimating Regression-Based Causal Effects Using Sufficient Dimension Reduction Wei Luo, Baruch College
3:35 PM Supervised Dimensionality Reduction for Exponential Family Data Andrew Landgraf, Battelle ; Yoonkyung Lee, The Ohio State University
 
 

73
Sun, 7/31/2016, 4:00 PM - 5:50 PM CC-W186a
Resistant and Outlier-Robust Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Carolyn Bradshaw Morgan, Hampton University
4:05 PM Using L_1 Data Depth Unsupervised Classifier for Detecting Communities in Networks Yahui Tian ; Yulia R. Gel, The University of Texas at Dallas
4:20 PM Fast and Robust Vertex Classification by Sequential Screening Li Chen, Intel Corporation ; Cencheng Shen, Temple University ; Carey Priebe, The Johns Hopkins University
4:35 PM Efficient Robust Regression with Variable Selection via Generalized Empirical Likelihood Sohini Raha, North Carolina State University ; Howard Bondell, North Carolina State University
4:50 PM Asymptotic Relative Efficiency for Robust Estimation of the Mean of Contaminated Graphs Under a Low Rank Model Runze Tang, The Johns Hopkins University ; Minh Tang, The Johns Hopkins University ; Michael Ketcha, The Johns Hopkins University ; Carey Priebe, The Johns Hopkins University ; Joshua Vogelstein, The Johns Hopkins University
5:05 PM Fully Efficient and Outlier-Robust Estimation in the Linear Mixed Model Won Gyo Suh, North Carolina State University ; Howard Bondell, North Carolina State University
5:20 PM A Model-Selection Criterion for Regression Estimators Based on Data Depth Subhabrata Majumdar, University of Minnesota - Twin Cities ; Snigdhansu Chatterjee, University of Minnesota - Twin Cities
5:35 PM Robust Clustering Methods for Time-Evolving Brain Signals Tianbo Chen, KAUST ; Ying Sun, King Abdullah University of Science and Technology ; Carolina Euan, CIMAT ; Hernando Ombao, University of California at Irvine
 
 

74
Sun, 7/31/2016, 4:00 PM - 5:50 PM CC-W186b
Estimation and Learning in Graphical Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Brian Naughton, North Carolina State University
4:05 PM Topological Property Hypotheses for Graphical Models Junwei Lu, Princeton ; Matey Neykov, Princeton ; Han Liu, Princeton
4:20 PM Learning Large-Scale DAG Models Using Overdispersion Gunwoong Park, University of Wisconsin - Madison
4:35 PM An Exposition on the Propriety of Restricted Boltzmann Machines Andrea Kaplan, Iowa State University ; Daniel Nordman, Iowa State University ; Stephen Vardeman, Iowa State University
4:50 PM Joint Multilevel Gaussian Graphical Model Liang Shan ; Inyoung Kim, Virginia Tech
5:05 PM A Convex Framework for High-Dimensional Sparse Cholesky Selection with Convergence Guarantees Syed Rahman, University of Florida ; Kshitij Khare, University of Florida
5:20 PM Topological Inference on Time-Varying Gaussian Graphical Models Kean Ming Tan ; Junwei Lu, Princeton ; Han Liu, Princeton ; Tong Zhang, Rutgers University
5:35 PM Blessing of Massive Scale: Spatial Graphical Model Estimation with a Total Cardinality Constraint Approach Ethan Fang, Princeton ; Han Liu, Princeton ; Mendi Wang, Princeton
 
 

88
Sun, 7/31/2016, 6:00 PM - 8:00 PM CC-Hall F1 West
The Extraordinary Power of Data — Invited Poster Presentations
Section on Statistical Learning and Data Science , Section on Statistical Graphics , Section on Statistics in Imaging , Business and Economic Statistics Section , Biometrics Section , ENAR , Section for Statistical Programmers and Analysts , Scientific and Public Affairs Advisory Committee , Section on Bayesian Statistical Science , Section on Statistics in Epidemiology , Section on Statistics in Marketing , Social Statistics Section , Statistics in Business Schools Interest Group
Chair(s): Tyler McCormick, University of Washington
1: Communicate Better with R, R Markdown, and Shiny Garrett Grolemund, RStudio
2: Spectral Filtering for Spatial-Temporal Dynamics Tian Zheng, Columbia University ; Lu Meng, Columbia University
3: A Mixed-Effects Modeling Approach to Study the Impact of Pesticides on Farmworkers' Brain Networks Using RS-fMRI Data Mohsen Bahrami, Virginia Tech ; Paul Laurienti, Wake Forest School of Medicine ; Thomas Arcury, Wake Forest School of Medicine ; Sean Simpson, Wake Forest School of Medicine
4: Cascaded High-Dimensional Histograms: A Generative Approach to Density Estimation Siong Thye Goh, MIT ; Cynthia Rudin, Duke University
5: TV Advertising's Impact on Online Searches Yonathan Schwarzkopf, Google ; Ying Liu, Google ; Makoto Uchida, Google ; Elissa Lee, Google ; Jim Koehler, Google
6: Modeling Connectivity in High-Dimensional Time Series Data via Factor Analysis Hernando Ombao, University of California at Irvine ; Yuxiao Wang, University of California at Irvine ; Chee-Ming Ting, Universiti Teknologi Malaysia
7: Analysis of Longitudinal Multi-Sequence MRI in Multiple Sclerosis Elizabeth M. Sweeney, Johns Hopkins Bloomberg School of Public Health ; Russell Shinohara, University of Pennsylvania ; John Muschelli, The Johns Hopkins University ; Daniel Reich , National Institute of Neurological Disorders and Stroke ; Ciprian Crainiceanu, The Johns Hopkins University ; Jonathan Gellar, Mathematica Policy Research ; Philip Reiss, New York University/University of Haifa ; Ani Eloyan, Brown University
8: Law, Order, and Algorithms Sharad Goel, Stanford University
9: Defining and Estimating Reliability in Hierarchical Logistic Regression Models for Health Care Provider Profiling — Jessica Hwang, RAND Corporation ; John Adams, Kaiser Permanente ; Susan M. Paddock, RAND Corporation
10: Probabilistic Cause-of-Death Assignment Using Verbal Autopsies Tyler McCormick, University of Washington ; Sam Clark, University of Washington ; Zehang Li, University of Washington
11: We Are What We Ask: Mapping the Ecosystem of Software Development Using Stack Overflow Data David G. Robinson, Stack Overflow
12: Data Science at Stitch Fix Hilary Parker, Stitch Fix
13: Text Mining on Domain Names Kenneth E. Shirley, Amazon
14: Fighting Fraud with Statistics! Alyssa Frazee, Stripe
15: Forecasting Seasonal Epidemics with Ensemble Methods and Collective Human Judgment Logan Conrad Brooks, Carnegie Mellon University ; Sangwon Hyun, Carnegie Mellon University ; Ryan Tibshirani, Carnegie Mellon University
16: Geometric Methods for Network Comparison and Multilevel Modeling Anna Smith, The Ohio State University ; Catherine Calder, The Ohio State University
17: Mixed-Effects Models for Resampled Network Statistics Improve Statistical Power to Find Differences in Functional Brain Connectivity Manjari Narayan, Rice University ; Genevera Allen, Rice University
18: Estimating the Causal Impact of Recommendation Systems from Observational Data Amit Sharma, Microsoft Research ; Jake Hofman, Microsoft Research ; Duncan Watts, Microsoft Research
19: The Future of the Journal Biostatistics — Dimitris Rizopoulos, Erasmus University Medical Center ; Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health
20: Sample Size Calculations for Micro-Randomized Trials in MHealth Peng Liao, University of Michigan ; Ji Sun, University of Michigan ; Susan A. Murphy, University of Michigan
 
 

Register 93
Mon, 8/1/2016, 7:00 AM - 8:15 AM CC-W375a
Section on Statistical Learning and Data Science A.M. Roundtable Discussion (Added Fee) — Roundtables AM Roundtable Discussion
Section on Statistical Learning and Data Science
Organizer(s): Genevera Allen, Rice University
ML06: What Can Statistics Learn from Machine Learning? And Vice Versa? — Ryan Tibshirani, Carnegie Mellon University ; Edward Henry Kennedy, Carnegie Mellon University
 
 

104 * !
Mon, 8/1/2016, 8:30 AM - 10:20 AM CC-W196a
Advanced Machine Learning Methods for Large-Scale Heterogeneous Data — Invited Papers
Section on Statistical Learning and Data Science , Royal Statistical Society , International Chinese Statistical Association
Organizer(s): Annie Qu, University of Illinois at Urbana-Champaign
Chair(s): Annie Qu, University of Illinois at Urbana-Champaign
8:35 AM Sparse Regression for Block Missing Data Without Imputation Yufeng Liu, The University of North Carolina at Chapel Hill
9:00 AM Automatic Summarization — Junhui Wang, University of Illinois at Chicago ; Xiaotong Shen, University of Minnesota ; Yiwen Sun, University of Minnesota ; Annie Qu, University of Illinois at Urbana-Champaign
9:25 AM Query-Specific Learning to Rank via Local Smoothing Junhui Wang, City University of Hong Kong
9:50 AM Is Manifold Learning (Finding Low-Dimensional Nonlinear Embeddings for High-Dimensional Data) Impractical for Large Data? Dominique Perrault-Joncas, Google ; James McQueen, University of Washington ; Marina Meila, University of Washington ; Zhongyue Zhang, University of Washington ; Jake VanderPlas, University of Washington
10:15 AM Floor Discussion
 
 

131
Mon, 8/1/2016, 8:30 AM - 10:20 AM CC-W195
Matrix Decomposition, Factor Models, and Applications to Recommender Systems — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Erin M. Schliep, University of Missouri
8:35 AM Orthogonal Symmetric Non-Negative Matrix Factorization Under Stochastic Block Model Subhadeep Paul, University of Illinois at Urbana-Champaign ; Yuguo Chen, University of Illinois at Urbana-Champaign
8:50 AM Sparse Spatial Dynamic Factor Model with Basis Expansion Takamitsu Araki ; Shotaro Akaho, National Institute of Advanced Industrial Science and Technology
9:05 AM A Statistical Algorithm for Phantom Clustering Using PPCA Wei Q. Deng, University of Toronto ; Radu V. Craiu, University of Toronto
9:20 AM Learning Network Dynamics via Regularized Tensor Decomposition Yun-Jhong Wu, University of Michigan ; Elizaveta Levina, University of Michigan ; Ji Zhu, University of Michigan
9:35 AM Incorporating Informative Missingness into a Regression-Based Recommender System Lin Su, North Carolina State University ; Howard Bondell, North Carolina State University
9:50 AM E-Learning Data Analysis for Building a Personalized Recommendation System Shuang Liu ; K.F. LAM, The University of Hong Kong
10:05 AM Tree-Like Structure Classification Based on Distance Matrix LU Decomposition with Application to Galaxy Profile Data Jianan Hui, University of California at Riverside ; Xinping Cui, University of California at Riverside ; James Flegal, University of California at Riverside ; Miguel Aragon-Calvo, University of California at Riverside
 
 

Register CE_17C
Mon, 8/1/2016, 8:30 AM - 5:00 PM CC-W471
Successful Data Mining in Practice (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Statistical Learning and Data Science
Instructor(s): Richard De Veaux, Williams College
8:30 AM Successful Data Mining in Practice (ADDED FEE) Richard De Veaux, Williams College
 
 

167 !
Mon, 8/1/2016, 10:30 AM - 12:20 PM CC-W175c
Methodological Advances and Applications of Finite Mixture Modeling — Topic Contributed Papers
Section on Statistical Learning and Data Science , Section on Statistical Computing
Organizer(s): Semhar Michael, South Dakota State University
Chair(s): Semhar Michael, South Dakota State University
10:35 AM Manly Transformation in Finite Mixture Modeling Xuwen Zhu, University of Alabama ; Volodymyr Melnykov, University of Alabama
10:55 AM Local Identifiability in Finite Mixture Model: A Gold Standard Solution? Daeyoung Kim, University of Massachusetts - Amherst
11:15 AM Modeling Right-Censored Loss Data Using Mixture of Distributions Tatjana Miljkovic, Miami University ; Semhar Michael, South Dakota State University ; Volodymyr Melnykov, University of Alabama
11:35 AM Model-Based Regression Clustering for High-Dimensional Data Emilie Devijver
11:55 AM Studying the Importance of Variables for Clustering Volodymyr Melnykov, University of Alabama ; Yana Melnykov, University of Alabama ; Xuwen Zhu, University of Alabama
12:15 PM Floor Discussion
 
 

176
Mon, 8/1/2016, 10:30 AM - 12:20 PM CC-W175b
Functional Data Analysis and Extensions to Manifold Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Umashanger Thayasivam, Rowan University
10:35 AM Supervised Functional Principal Component Analysis Yunlong Nie, Simon Fraser University ; Jiguo Cao, Simon Fraser University
10:50 AM A Geometric Approach to Confidence Regions and Bands for Functional Data Hyunphil Choi, Penn State University ; Matthew Reimherr, Penn State University
11:05 AM Manifold Data Analysis Hyun Bin Kang ; Matthew Reimherr, Penn State University
11:20 AM Functional Statistical Process Control Using Elastic Methods James Derek Tucker, Sandia National Laboratories
11:35 AM Scalars-on-Function Linear Regression with Large Number of Functional Predictors Ruiyan Luo ; Xin Qi, Georgia State University
11:50 AM Nonlinear Function on Function Regression with Multiple Prediction Curves Xin Qi, Georgia State University ; Ruiyan Luo
12:05 PM Manifold Learning: Dimension Reduction Versus Parameterization Recovery — Michael Trosset, Indiana University ; Lijiang Guo, Indiana University
 
 

248
Mon, 8/1/2016, 2:00 PM - 3:50 PM CC-W185a
Bayesian Methods for Shrinkage in High-Dimensional and Complex Data — Contributed Papers
Section on Statistical Learning and Data Science , International Society for Bayesian Analysis (ISBA) , Section on Bayesian Statistical Science
Chair(s): Jennifer Sniadecki, Ultragenyx
2:05 PM A Variational Bayesian Algorithm for Variable Selection Xichen Huang ; Jin Wang, University of Illinois ; Feng Liang, University of Illinois at Urbana-Champaign
2:20 PM Bayesian Regression Using a Prior on the Model Fit Brian Naughton, North Carolina State University ; Howard Bondell, North Carolina State University
2:35 PM High-Dimensional Linear Regression via the R2-D2 Shrinkage Prior Yan Zhang, North Carolina State University ; Brian J. Reich, North Carolina State University ; Howard Bondell, North Carolina State University
2:50 PM A Topic Model for Hierarchical Documents Feifei Wang ; Yang Yang, Peking University
3:05 PM Selecting the Number of Topics in a Latent Dirichlet Allocation Topic Model Dale Bowman, University of Memphis
3:20 PM Modeling Bipartite Graph Using Dependent Indian Buffet Processes Ketong Wang, University of Alabama ; Michael D. Porter, University of Alabama
3:35 PM Adaptively Choosing Hyperparameters for Non-Local Priors in High Dimensional Bayesian Variable Selection Amir Nikooienejad, Texas A&M University ; Valen E. Johnson, Texas A&M University
 
 

249
Mon, 8/1/2016, 2:00 PM - 3:50 PM CC-W186a
Regularization and Prediction in Time Series and Longitudinal Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Chuck Kincaid, Experis
2:05 PM Joint Modeling of Correlated Binary Response and Longitudinal Covariates via Random Forest Applied to Glaucoma Progression Prediction Juanjuan Fan, San Diego State University ; Lucie Sharpsten, United Health Group ; Xiaogang Su, The University of Texas at El Paso ; Shaban Demirel, Devers Eye Institute ; Richard Levine, San Diego State University
2:20 PM Data Representation and Pattern Recognition in Financial Time Series Seunghye Jung Wilson, George Mason University ; James E. Gentle, George Mason University
2:35 PM Estimation of Multi-Granger Network Causal Models Andrey Skripnikov, University of Florida ; George Michailidis, University of Florida
2:50 PM High-Dimensional Regularized Estimation in Time Series Under Mixing Conditions Kam Chung Wong, University of Michigan ; Ambuj Tewari, University of Michigan ; Zifan Li, University of Michigan
3:05 PM Next-Generation Flow Field Forecasting: Organizing and Searching Historical Time Series Data Using CART Kyle Caudle, South Dakota School of Mines and Technology ; Michael Frey, Bucknell University ; Patrick Fleming, South Dakota School of Mines and Technology
3:20 PM Longitudinal Network Prediction with Applications to Network-Based Interventions Ravi Goyal, Mathematica Policy Research
3:35 PM Applications of Machine Learning in Environmetrics: Detecting Dynamic Trend-Based Clusters Xin Huang, The University of Texas at Dallas ; Iliyan R. Iliev, The University of Texas at Dallas ; Lyubchich Vyacheslav, University of Maryland Center for Environmental Science ; Alexander Brenning , University of Jena ; Yulia R. Gel, The University of Texas at Dallas
 
 

Register 277
Tue, 8/2/2016, 7:00 AM - 8:15 AM CC-W375a
Section on Statistical Learning and Data Science A.M. Roundtable Discussion (Added Fee) — Roundtables AM Roundtable Discussion
Section on Statistical Learning and Data Science
Organizer(s): Genevera Allen, Rice University
TL07: Data Science: Bridging Academia and Industry Justin Dyer, Google ; Donal McMahon, Google
 
 

298 * !
Tue, 8/2/2016, 8:30 AM - 10:20 AM CC-W187c
Regularization: A Versatile Technique for High-Dimensional Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science , Korean International Statistical Society
Organizer(s): Cheolwoo Park, University of Georgia
Chair(s): Seunggeun Lee, University of Michigan
8:35 AM Regularized LDA for High-Dimensional Data Jeongyoun Ahn, University of Georgia ; Yongho Jeon, Yonsei University
8:55 AM Variable Selection for Haitian Tuberculosis (TB) Patients Metabolomics Studies Myung Hee Lee, Weill Cornell Medicine
9:15 AM Principal Quantile Regression for Sufficient Dimension Reduction with Heteroscedasticity Chong Wang, North Carolina State University ; Yichao Wu, North Carolina State University ; Seeing Jun Shin, Korea University
9:35 AM Sparse Additive Graphical Models Hyonho Chun, Purdue University ; Ji Hwan Oh, Purdue University
9:55 AM Discussant: Woncheol Jang, Seoul National University
10:15 AM Floor Discussion
 
 

314
Tue, 8/2/2016, 8:30 AM - 10:20 AM CC-W187a
Regularization Methods for Sparsity and Smoothness — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jie Yang, University of Illinois at Chicago
8:35 AM Scaled Concave Penalized Regression Long Feng ; Cun-Hui Zhang, Rutgers University
8:50 AM Hierarchical Sparse Modeling: A Choice of Two Regularizers Xiaohan Yan, Cornell University ; Jacob Bien, Cornell University
9:05 AM Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High-Dimensional Mediators Yi Zhao, Brown University ; Xi Luo, Brown University
9:20 AM Stagewise Generalized Estimating Equations Gregory Vaughan, University of Connecticut ; Robert Aseltine, University of Connecticut Health Center ; Kun Chen, University of Connecticut ; Jun Yan, University of Connecticut
9:35 AM CoCoLasso for High-Dimensional Error-in-Variables Regression Abhirup Datta, University of Minnesota ; Hui Zou, University of Minnesota
9:50 AM Risk Estimation for High-Dimensional Lasso Regression Daniel McDonald, Indiana University ; Darren Homrighausen, Colorado State University
10:05 AM Nonparametric Regression with Adaptive Smoothness via a Convex Hierarchical Penalty Asad Haris, University of Washington ; Ali Shojaie, University of Washington ; Noah Simon, University of Washington
 
 

315
Tue, 8/2/2016, 8:30 AM - 10:20 AM CC-W186b
Complex and Multiscale Network Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Yan Zhang, North Carolina State University
8:35 AM Hypothesis Testing for Detecting Changes Within a Barabási-Albert Network Fairul Mohd-Zaid, Air Force Research Lab ; Christine Schubert Kabban, Air Force Institute of Technology ; Edward White, Air Force Institute of Technology ; Richard Deckro, Air Force Institute of Technology
8:50 AM Multiscale Network Analysis Using an Adaptive Haar-Like Transformation Xinyu Kang, Boston University ; Piotr Fryzlewicz, London School of Economics ; Eric D. Kolaczyk, Boston University
9:05 AM Network Degree Distribution Inference Under Sampling Aleksandrina Goeva, Boston University ; Richard Lehoucq, Sandia National Laboratories ; Eric D. Kolaczyk, Boston University
9:20 AM A Blockmodel for Node Popularity in Networks with Community Structure Srijan Sengupta, University of Illinois at Urbana-Champaign ; Yuguo Chen, University of Illinois at Urbana-Champaign
9:35 AM A Point Process Model with Latent Positions for Network Modeling Bomin Kim
9:50 AM Network Cross-Validation by Edge Sampling Tianxi Li, University of Michigan ; Elizaveta Levina, University of Michigan ; Ji Zhu, University of Michigan
10:05 AM Uncertainty Assessment for Source Estimation of Spreading Processes on Complex Networks Juliane Manitz, Boston University ; Jun Li, Boston University ; Eric D. Kolaczyk, Boston University
 
 

344 * !
Tue, 8/2/2016, 10:30 AM - 12:20 PM CC-W183c
SLDS 2016 Student Paper Awards Session — Topic Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Organizer(s): Tian Zheng, Columbia University
Chair(s): Tian Zheng, Columbia University
10:35 AM A Group-Specific Recommender System Xuan Bi ; Annie Qu, University of Illinois at Urbana-Champaign ; Junhui Wang, City University of Hong Kong ; Xiaotong Shen, University of Minnesota
10:55 AM Scalable Bayesian Rule Lists Hongyu Yang, MIT EECS ; Cynthia Rudin, Duke University ; Margo Seltzer, Harvard
11:15 AM Model-Based Clustering for Large-Scale Dynamic Networks Kevin Lee, Penn State University ; Lingzhou Xue, Penn State University ; David Hunter, Penn State University
11:35 AM Sparse Multidimensional Graphical Models: A Unified Bayesian Framework Yang Ni ; Francesco Stingo, MD Anderson Cancer Center ; Veera Baladandayuthapani, MD Anderson Cancer Center
11:55 AM Another Look at Distance-Weighted Discrimination Boxiang Wang, University of Minnesota ; Hui Zou, University of Minnesota
12:15 PM Floor Discussion
 
 

353
Tue, 8/2/2016, 10:30 AM - 12:20 PM CC-W181a
SPEED: Statistical Learning and Data Science — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Michael Weylandt, Rice University
10:35 AM A Random Forest of Modified Interaction Trees for Treatment Decision Rules Zhen Zeng, Merck ; Zheng Wei, Sanofi US ; Yuefeng Lu, Sanofi US
10:40 AM Modern Projection Pursuit Ellipse for High-Dimensional Data Jang Ik Cho, Case Western Reserve University ; Xiaoyan Wei, Case Western Reserve University ; Jiayang Sun, Case Western Reserve University
10:45 AM Model-Free Estimation of Task-Based Dynamic Functional Connectivity and Its Confidence Intervals Maria Kudela, Indiana University ; Mario Dzemidzic, Indiana University School of Medicine ; Brandon G. Oberlin, Indiana University School of Medicine ; Joaquín Goñi, Purdue University ; David A. Kareken, Indiana University School of Medicine ; Jaroslaw Harezlak, Indiana University Fairbanks School of Public Health
10:50 AM Topological Statistical Inference for Location Parameters via Frechet Functions Ruite Guo, Florida State University
10:55 AM An R Package Enabling Likelihood-Based Inference for Generalized Linear Mixed Models Christina Knudson
11:00 AM Community Extraction for Networks with Node Covariates via Pseudo-Likelihood Method Chengan Du, George Mason University ; Qing Pan, The George Washington University ; Yunpeng Zhao, George Mason University
11:05 AM Predicting Job Application Success with Two-Stage, Bayesian Modeling of Features Extracted from Candidate-Role Pairs Jon Krohn, untapt ; Gabe Rives-Corbett, untapt ; Ed Donner, untapt
11:10 AM Likelihood Methods for Non-Negative Matrix Factorization Frank Shen, Penn State University
11:15 AM Models for Understanding and Predicting Consumer Perception of Radiance Supriya A. K. Satwah, Unilever ; Anthony Cece, Unilever ; Robert Velthuizen, Unilever
11:30 AM High-Dimensional Inference for Partial Linear Models Zhuqing Yu
11:35 AM Penalized Principal Logistic Regression for Sparse Sufficient Dimension Reduction Seung Jun Shin, Korea University ; Andreas Artemiou, Cardiff University
11:40 AM Dimension-Reduction Techniques for Predictive Modeling Zhen Zhang, C Spire ; Lei Zhang, Mississippi State Department of Health ; Kendell Churchwell, C Spire ; James Veillette, C Spire
11:50 AM The Knockoff Filter for FDR Control in Group-Sparse and Multitask Regression Ran Dai, The University of Chicago ; Rina Foygel Barber, The University of Chicago
11:55 AM Maximizing Text Mining Performance: The Impact of Pre-Processing Dario Gregori, University of Padova ; Paola Berchialla, University of Torino ; Nicola Soriani, University of Padova ; Ileana Baldi, University of Padova ; Corrado Lanera, University of Padova
 
 

361
Tue, 8/2/2016, 10:30 AM - 12:20 PM CC-W186b
Applications of Ensemble and Tree-Based Methods — Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Chair(s): Dirk Moore, Rutgers School of Public Health
10:35 AM Decoding Brain States from fMRI Data with a Machine Learning Method Elizabeth Chou
10:50 AM Classification and Regression Tree Modeling of Correlated Binary Outcomes Jaime Speiser, Medical University of South Carolina ; Valerie Durkalski-Mauldin, Medical University of South Carolina ; Dongjun Chung, Medical University of South Carolina ; Bethany Wolf, Medical University of South Carolina
11:05 AM Ranking Homologous Proteins Using an Ensemble of Logistic Regression Models Based on Subsets of Feature Variables Jabed Tomal, University of Toronto ; William J. Welch, The University of British Columbia ; Ruben H. Zamar, The University of British Columbia
11:20 AM An Approach to the Multivariate Two-Sample Problem Using Classification and Regression Trees and Minimum-Weight Spanning Subgraphs David Ruth ; Samuel Buttrey, Naval Postgraduate School ; Lyn Whitaker, Naval Postgraduate School
11:35 AM Methodological Strategies to Define a Generalizable Model for Machine Learning Ensemble Techniques Joel Correa da Rosa, Rockefeller University ; Lewis Tomalin, Icahn School of Medicine at Mount Sinai ; Mayte Suárez-Fariñas, Icahn School of Medicine at Mount Sinai
11:50 AM Selecting Decision Rules from Tree Ensembles Damir Spisic, IBM ; Jing Xu, IBM
12:05 PM Floor Discussion
 
 

406 * !
Tue, 8/2/2016, 2:00 PM - 3:50 PM CC-W180
Recent Advances in High-Dimensional Statistics and Computational Methods — Invited Papers
Section on Statistical Learning and Data Science , Section on Statistics in Imaging
Organizer(s): Dan Yang, Rutgers University
Chair(s): Po-Ling Loh, University of Pennsylvania
2:05 PM Oracle Inequalities for Network Models and Sparse Graphon Estimation — Alexandre Tsybakov, ENSAE ; Olga Klopp, University Paris 10/CREST ; Nicolas Verzelen, INRA
2:25 PM Bilinear Regression with Matrix Covariates in High Dimensions Dan Yang, Rutgers University ; Dong Wang ; Hongtu Zhu, The University of North Carolina at Chapel Hill ; Haipeng Shen, The University of Hong Kong
2:45 PM Convex Regularization for High-Dimensional Tensor Regression Ming Yuan, University of Wisconsin ; Garvesh Raskutti, University of Wisconsin - Madison
3:05 PM Why Do Statisticians Treat Predictors as Fixed? A Conspiracy Theory Andreas Buja, The Wharton School ; Richard Berk, The Wharton School ; Lawrence D. Brown, University of Pennsylvania ; Edward I. George, The Wharton School ; Emil Pitkin, The Wharton School ; Mikhail Traskin, Amazon.com ; Linda Zhao, The Wharton School ; Kai Zhang, The University of North Carolina at Chapel Hill
3:25 PM Sum of Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems Yash Deshpande, Stanford University
3:45 PM Floor Discussion
 
 

432
Tue, 8/2/2016, 2:00 PM - 3:50 PM CC-W178b
New Approaches in Classification Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jason Gillikin, Priority Health
2:05 PM L-CC Classification and Variable Selection for Multi-Label Data Sets Monika Stupalova du Toit, Stellenbosch University ; Sarel Steel, Stellenbosch University
2:20 PM Multi-Class ROC Tree and Random Forest for Imbalanced Data Classification Jiaju Yan, SUNY Stony Brook ; Wei Zhu, SUNY Stony Brook ; Bowen Song, Ocean University of China
2:35 PM Efficient Sampling Strategy for SVM Through Semi-Supervised Active Learning Yaru Shi, University of Illinois at Chicago ; Yoonsang Kim, University of Illinois at Chicago ; Ganna Kostygina, University of Illinois at Chicago ; Sherry Emery, University of Illinois at Chicago
2:50 PM Feature Selection for Class-Imbalanced Data Using Binormal Precision-Recall Curves Zhongkai Liu, North Carolina State University ; Howard Bondell, North Carolina State University
3:05 PM Nonparametric Classification Using a Forest Dependency Structure Mary Frances Dorn, Texas A&M University ; Clifford Spiegelman, Texas A&M University ; Amit Moscovich, Weizmann Institute of Science ; Boaz Nadler, Weizmann Institute of Science
3:20 PM Statistical Learning Toolbox for Prediction Umashanger Thayasivam, Rowan University
3:35 PM Big Data Methods for Scraping Government Tax Revenue from the Web Brian Dumbacher, U.S. Census Bureau ; Cavan Capps, U.S. Census Bureau
 
 

433
Tue, 8/2/2016, 2:00 PM - 3:50 PM CC-W182
Random Graph and Network Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Barbara J. Robles, Federal Reserve Board
2:05 PM Exponential Random Graph Models with Stable (Nonparametric) Statistics Goeran Kauermann, Ludwig-Maximilians-University Munich
2:20 PM Generalized Exponential Random Graph Models: Statistical Inference for Weighted Graphs James D. Wilson, University of San Francisco ; Shankar Bhamidi, The University of North Carolina at Chapel Hill
2:35 PM Reconstruction of Directed Acyclic Graphs Networks Based on Prior Causal Ordering Information with Applications to Gene Regulatory Networks Pei-Li Wang, University of Florida ; George Michailidis, University of Florida
2:50 PM Link Prediction via Matrix Decomposition by Solving Lyapunov Equations Yunpeng Zhao, George Mason University
3:05 PM Risk, Value, and Popularity: A Network-Based Approach to Stock Portfolio Diversification Natallia Katenka, University of Rhode Island ; Gregory Breard, University of Rhode Island
3:20 PM Lasso-Type Network Community Detection Within Latent Space Shiwen Shen ; Edsel Aldea Pena, University of South Carolina
3:35 PM Floor Discussion
 
 

445
Tue, 8/2/2016, 2:00 PM - 3:50 PM CC-Hall F1 West
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science , Section on Statistics in Imaging , International Chinese Statistical Association
Chair(s): Genevera Allen, Rice University
29: Angle-Based Distance-Weighted Support Vector Machine in Multicategory Classification Hui Sun, Purdue University ; Bruce A. Craig, Purdue University ; Lingsong Zhang, Purdue University
30: A New Distribution to Describe Big Data Yuanyuan Zhang, University of Manchester
32: Identification of Solids in Hyperspectral Images Using Spectral Features from Gaussian Basis Functions Cory Lanker, Lawrence Livermore National Laboratory ; Milton O. Smith, Lawrence Livermore National Laboratory
33: ZIP Codes and Neural Networks: Machine Learning for Handwritten Number Recognition Cuixian Chen, The University of North Carolina at Wilmington ; Taylor Harbold, The University of North Carolina at Wilmington ; Courtney Rasmussen, The University of North Carolina at Wilmington ; Michelle Page, The University of North Carolina at Wilmington
34: Constrained Canonical Covariance Analysis by Using Tucker2 Model Jun Tsuchida, Doshisha University ; Hiroshi Yadohisa, Doshisha University
35: Sparse Predictive Modeling for Bank Telemarketing Success Using Smooth-Threshold Estimating Equations Yoshinori Kawasaki, Institute of Statistical Mathematics ; Masao Ueki, Kurume University
37: Predicting Binary Outcome with Unequal Misclassification Cost Shuchismita Sarkar, University of Alabama ; Michael D. Porter, University of Alabama
39: Empirical Evaluation of Bayesian Network Classifiers Weihua Shi, SAS Institute
40: Employing Machine Learning Approaches in Social Scientific Analyses — Arne Bethmann, Institute for Employment Research ; Jonas Beste, Institute for Employment Research
41: A Multilocus Genetic Score for Physical Activity Lingyao Yang, Stanford University ; Haley Hedlin, Stanford University
42: Anomaly Detection in Time Series of Dependent Stochastic Block Model Graphs — Heng Wang, Machine Zone ; Albert Liu, Ward Melville High School ; Youngser Park, The Johns Hopkins University ; Carey Priebe, The Johns Hopkins University
43: Clustering for Personalized Preference Prediction Fan Yang, University of Minnesota - Twin Cities ; Xiaotong Shen, University of Minnesota
46: Divide and Recombine (DandR) with Tessera: High-Performance Computing for the Analysis of Big Data and High-Complexity Analytics Yuying Song, Purdue University ; Bowei Xi, Purdue University ; Ryan Hafen, Hafen Consulting ; William S. Cleveland, Purdue University
48: Understanding Grand Strategy: Text and Topic Analysis of Presidential Speeches Reagan Rose
49: AMON: An Open Source Architecture for Online Monitoring, Statistical Analysis, and Forensics of Multi-Gigabit Streams Shrijita Bhattacharya, University of Michigan ; Michael Khallitsis, Merit
50: Estimating Coefficients of Direction in Single Index Model for Large $P$ Small $N$ Problem Jin Xie, University of Kentucky ; Xiangrong Yin, University of Kentucky
51: C.Logic: An Algorithm to Classify Dichotomous Disease Outcomes Using Interactions Between Dichotomous and Continuous Predictors Sybil Prince Nelson
52: Hypothesis Testing and Prediction of the Self-Triggering Cox Model for Recurrent Event Data Jung In Kim ; Jason Fine, The University of North Carolina at Chapel Hill ; Feng-Chang Lin, The University of North Carolina at Chapel Hill
53: Regret Bounds for a Thompson Sampling Algorithm with Application to Emerging Infectious Disease Tao Hu, North Carolina State University ; Eric Laber, North Carolina State University
54: Group Discrimination Using Sparse Network Modeling of Resting-State fMRI Maria Puhl, University of Tulsa ; William Coberly, University of Tulsa ; Alejandro Hernandez, University of Tulsa ; Kyle Simmons, Laureate Institute for Brain Research
 
 

449
Tue, 8/2/2016, 2:00 PM - 2:45 PM CC-Hall F1 West
SPEED: Statistical Learning and Data Science, Part 2A — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Genevera Allen, Rice University
11: A Random Forest of Modified Interaction Trees for Treatment Decision Rules Zhen Zeng, Merck ; Zheng Wei, Sanofi US ; Yuefeng Lu, Sanofi US
12: Modern Projection Pursuit Ellipse for High-Dimensional Data Jang Ik Cho, Case Western Reserve University ; Xiaoyan Wei, Case Western Reserve University ; Jiayang Sun, Case Western Reserve University
13: Model-Free Estimation of Task-Based Dynamic Functional Connectivity and Its Confidence Intervals Maria Kudela, Indiana University ; Mario Dzemidzic, Indiana University School of Medicine ; Brandon G. Oberlin, Indiana University School of Medicine ; Joaquín Goñi, Purdue University ; David A. Kareken, Indiana University School of Medicine ; Jaroslaw Harezlak, Indiana University Fairbanks School of Public Health
14: Topological Statistical Inference for Location Parameters via Frechet Functions Ruite Guo, Florida State University
15: An R Package Enabling Likelihood-Based Inference for Generalized Linear Mixed Models Christina Knudson
16: Community Extraction for Networks with Node Covariates via Pseudo-Likelihood Method Chengan Du, George Mason University ; Qing Pan, The George Washington University ; Yunpeng Zhao, George Mason University
17: Predicting Job Application Success with Two-Stage, Bayesian Modeling of Features Extracted from Candidate-Role Pairs Jon Krohn, untapt ; Gabe Rives-Corbett, untapt ; Ed Donner, untapt
18: Likelihood Methods for Non-Negative Matrix Factorization Frank Shen, Penn State University
20: Models for Understanding and Predicting Consumer Perception of Radiance Supriya A. K. Satwah, Unilever ; Anthony Cece, Unilever ; Robert Velthuizen, Unilever
The oral portion will take place during Session 213151
 
 

452
Tue, 8/2/2016, 3:05 PM - 3:50 PM CC-Hall F1 West
SPEED: Statistical Learning and Data Science, Part 2B — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Genevera Allen, Rice University
11: High-Dimensional Inference for Partial Linear Models Zhuqing Yu
12: Penalized Principal Logistic Regression for Sparse Sufficient Dimension Reduction Seung Jun Shin, Korea University ; Andreas Artemiou, Cardiff University
13: Dimension-Reduction Techniques for Predictive Modeling Zhen Zhang, C Spire ; Lei Zhang, Mississippi State Department of Health ; Kendell Churchwell, C Spire ; James Veillette, C Spire
16: The Knockoff Filter for FDR Control in Group-Sparse and Multitask Regression Ran Dai, The University of Chicago ; Rina Foygel Barber, The University of Chicago
17: Maximizing Text Mining Performance: The Impact of Pre-Processing Dario Gregori, University of Padova ; Paola Berchialla, University of Torino ; Nicola Soriani, University of Padova ; Ileana Baldi, University of Padova ; Corrado Lanera, University of Padova
The oral portion will take place during Session 213151
 
 

213592
Tue, 8/2/2016, 5:00 PM - 6:00 PM H-Stevens Salon C 5
Section on Statistical Learning and Data Science Business Meeting — Other Cmte/Business
Section on Statistical Learning and Data Science
Chair(s): Richard De Veaux, Williams College
 
 

213607
Tue, 8/2/2016, 6:00 PM - 7:30 PM H-Stevens Salon C 5
Section on Statistical Learning and Data Science Mixer — Other Cmte/Business
Section on Statistical Learning and Data Science
Chair(s): Richard De Veaux, Williams College
 
 

Register 461
Wed, 8/3/2016, 7:00 AM - 8:15 AM CC-W375a
Section on Statistical Learning and Data Science A.M. Roundtable Discussion (Added Fee) — Roundtables AM Roundtable Discussion
Section on Statistical Learning and Data Science
Organizer(s): Genevera Allen, Rice University
WL07: Members Choice: Hot Topics in Statistical Learning and Data Mining Glen Wright Colopy, University of Oxford
 
 

485 * !
Wed, 8/3/2016, 8:30 AM - 10:20 AM CC-W194b
Recent Advances in Functional Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Organizer(s): Xiaoke Zhang, University of Delaware
Chair(s): Raymond Wong, Iowa State University
8:35 AM Single-Index Models for Function-on-Function Regression Guanqun Cao, Auburn University ; Li Wang, Iowa State University
8:55 AM Weighing Schemes for Functional Data Xiaoke Zhang, University of Delaware ; Jane-Ling Wang, University of California at Davis
9:15 AM Historical Functional Cox Regression, with an Application to Prediction of Multiple Sclerosis Lesions Philip Reiss, New York University ; Elizabeth M. Sweeney, Johns Hopkins Bloomberg School of Public Health ; Jonathan Gellar, Mathematica Policy Research
9:35 AM Nonparametric Estimation of Stationary Covariance Functions Li Wang, Iowa State University ; Jiangyan Wang, Soochow University ; Guanqun Cao, Auburn University
9:55 AM Functional Data Methods for Child Growth Data Luo Xiao, North Carolina State University ; Andrew Leroux, Johns Hopkins Bloomberg School of Public Health ; William Checkley, The Johns Hopkins University ; Ciprian Crainiceanu, The Johns Hopkins University
10:15 AM Floor Discussion
 
 

498
Wed, 8/3/2016, 8:30 AM - 10:20 AM CC-W194a
Community, Social, and Anomaly Detection in Networks — Contributed Papers
Section on Statistical Learning and Data Science , Royal Statistical Society
Chair(s): Georgiy Bobashev, RTI International
8:35 AM Self-Similarity Estimation for Cyber-Security Marina Evangelou, Imperial College London ; Niall Adams, Imperial College London
8:50 AM Analysis of Community Evolution in Networks Giuliana Pallotta, Lawrence Livermore National Laboratory ; Goran Konjevod, Lawrence Livermore National Laboratory
9:05 AM Anomaly Detection in Time-Evolving Networks Using Tensor Spectrum Ruikai Cao, The University of Texas at Dallas ; Yulia R. Gel, The University of Texas at Dallas
9:20 AM Structural Balance in Village Social Networks with Antagonistic Ties Derek Feng, Yale University
9:35 AM Learning the Underlying Social Network from Continuous-Time Pairwise Interaction Data Wesley Lee, University of Washington ; Bailey Fosdick, Colorado State University ; Tyler McCormick, University of Washington
9:50 AM An ROC Approach to Estimating Interpersonal Networks Deniz Yenigun, Istanbul Bilgi University ; Gunes Ertan, Koc University ; Michael Siciliano, University of Illinois at Chicago
10:05 AM Floor Discussion
 
 

499
Wed, 8/3/2016, 8:30 AM - 10:20 AM CC-W193b
Statistical Learning Approaches to Biological Inference Problems — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Nusrat Jahan, James Madison University
8:35 AM Incorporating Biological Information in Sparse Principal Component Analysis with Application to Genomic Data Ziyi Li, Emory University ; Qi Long, Emory University ; Sandra Safo, Emory University
8:50 AM Detecting Real-Time Substance Use from Wearable Biosensor Data Stream Chanpaul Jin Wang, University of Massachusetts Medical School ; Hua Fang, University of Massachusetts Medical School ; Stephanie Carreiro, University of Massachusetts Medical School ; Honggang Wang, University of Massachusetts - Dartmouth ; Edward Boyer, University of Massachusetts Medical School
9:05 AM Statistics and Machine Learning in Pharmacovigilance for Signal Detection of Cardiovascular Risks James Chen, FDA/NCTR ; Weizhong Zhao , FDA/NCTR ; Wen Zou, FDA/NCTR
9:20 AM Deep Spatial Learning for Forensic Geolocation with Microbiome Data Neal Grantham ; Brian J. Reich, North Carolina State University ; Eric Laber, North Carolina State University
9:35 AM Human Detection from Images with Supervised Kernel PCA Yishi Wang, The University of North Carolina at Wilmington ; Troy Kling, University of Florida
9:50 AM Genome-Wide Association Studies Using a Penalized Moving-Window Regression Minli Bao, University of Iowa ; Kai Wang, University of Iowa
10:05 AM Data Normalization by Fisher-Yates Transformation Yayan Zhang, Merck ; Javier Cabrera, Rutgers University ; Birol Emir, Pfizer Inc & Columbia University
 
 

518 !
Wed, 8/3/2016, 10:30 AM - 12:20 PM CC-W179a
Getting High on Statistics: Powering Large-Scale Data Analysis — Invited Papers
Section on Statistical Learning and Data Science
Organizer(s): Ananda Sen, University of Michigan
Chair(s): Ananda Sen, University of Michigan
10:35 AM Mixed Graphical Model Selection with Applications to Integrative Genomics Genevera Allen, Rice University ; Yulia Baker, Rice University
11:05 AM A Hypothesis-Testing Framework for Modularity-Based Network Community Detection Jingfei Zhang, University of Miami ; Yuguo Chen, University of Illinois at Urbana-Champaign
11:35 AM Iterative Random Forests: Stable Identification of High-Order Interactions in Heterogeneous and High-Dimensional Data Sumanta Basu, University of California at Berkeley ; Bin Yu, University of California at Berkeley
12:05 PM Floor Discussion
 
 

544
Wed, 8/3/2016, 10:30 AM - 12:20 PM CC-W178b
Statistical Learning with Censored Data and Systematic Sampling — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jonathan Hobbs, Jet Propulsion Laboratory
10:35 AM Modeling an Ordinal Outcome in High Dimensions with Nonparametric Feature Augmentation and Proportional Odds Boosting Kyle Ferber, Virginia Commonwealth University ; Kellie J. Archer, Virginia Commonwealth University
10:50 AM An approximate L0-based variable selection method for high dimensional data Zhihua Sun, Ocean University of China ; Gang Li, University of California at Los Angeles
11:05 AM Selection for Semiparametric Odds Ratio Model via Adaptive Screening Jinsong Chen, University of Illinois at Chicago ; Huayun Chen, University of Illinois at Chicago
11:20 AM Using Inverse Probability of Censoring Weighted Bagging to Adapt Machine-Learning Techniques to Censored Data Ales Kotalik, University of Minnesota ; Julian Wolfson, University of Minnesota ; David Vock, University of Minnesota School of Public Health ; Gediminas Adomavicius, University of Minnesota ; Sunayan Bandyopadhyay, University of Minnesota
11:35 AM Bayesian Neural Network for Predicting Survival Time of Competing Risks Taysseer Sharaf, Slippery Rock University
11:50 AM Systematic Sampling Design with Application to Data Splitting Redouane Betrouni, George Mason University ; James E. Gentle, George Mason University
12:05 PM Statistical Computation with Mixture Data Shiju Zhang, St. Cloud State University
 
 

608
Wed, 8/3/2016, 2:00 PM - 3:50 PM CC-W181a
Variable Selection Methods for Sparse Learning from Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Bethany Wolf, Medical University of South Carolina
2:05 PM Group Feature Selection in Ultrahigh-Dimensional Generalized Varying-Coefficient Linear Models Songshan Yang ; Runze Li, Penn State University
2:20 PM Testing-Based Variable Selection for High-Dimensional Linear Models Siliang Gong, The University of North Carolina at Chapel Hill ; Kai Zhang, The University of North Carolina at Chapel Hill ; Yufeng Liu, The University of North Carolina at Chapel Hill
2:35 PM Subsampling for Feature Selection from Large Regression Data Yiying Fan, Cleveland State University ; Jiayang Sun, Case Western Reserve University
2:50 PM Flexible Modeling of Local Dependence in Variables with a Natural Ordering Guo Yu, Cornell University ; Jacob Bien, Cornell University
3:05 PM Sparsity-Oriented Importance Learning Chenglong Ye, University of Minnesota ; Yi Yang, McGill University ; Yuhong Yang, University of Minnesota
3:20 PM ThrEEBoost: Thresholded Boosting for Variable Selection and Prediction via Estimating Equations Benjamin Brown
3:35 PM Floor Discussion
 
 

609
Wed, 8/3/2016, 2:00 PM - 3:50 PM CC-W181b
Spatio-Temporal Models, Prediction, and Anomaly Detection — Contributed Papers
Section on Statistical Learning and Data Science , Royal Statistical Society
Chair(s): Sarah Kalicin, Intel Corporation
2:05 PM Point Process Modeling with Spatiotemporal Covariates for Predicting Crime Alex Reinhart, Carnegie Mellon University ; Xizhen Cai, Carnegie Mellon University ; Joel Greenhouse, Carnegie Mellon University
2:20 PM Identification of Homogeneous Areas Through Lattice-Based Spatio-Temporal Clustering Rodrigue Ngueyep Tzoumpe, IBM Research ; Huijing Jiang, IBM ; YoungDeok Hwang, IBM T. J. Watson Research Center
2:35 PM Generalized Difference in Difference Models with Gaussian Processes William Herlands, Carnegie Mellon University ; Daniel B. Neill, Carnegie Mellon University ; Akshaya Jha, Carnegie Mellon University ; Seth Flaxman, University of Oxford ; Kun Zhang, Carnegie Mellon University
2:50 PM Identifying Typical Patterns and Atypical Behavior in Copious Amounts of Streaming Data Brett Amidan, Pacific Northwest National Laboratory ; James Follum, Pacific Northwest National Laboratory
3:05 PM Archetypal Analysis: Three Case Studies Anna Quach ; Adele Cutler, Utah State University
3:20 PM Vertex Nomination via Seeded Graph Matching Heather Patsolic, The Johns Hopkins University ; Vince Lyzinski, The Johns Hopkins University ; Carey Priebe, The Johns Hopkins University
3:35 PM Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection Edward McFowland, Carlson School of Management ; Sriram Somanchi, University of Notre Dame ; Daniel B. Neill, Carnegie Mellon University
 
 

641 * !
Thu, 8/4/2016, 8:30 AM - 10:20 AM CC-W180
Advancing Precision Medicine Using Innovative Subgroup Identification Methods — Topic Contributed Papers
Section on Statistical Learning and Data Science , Biopharmaceutical Section , International Chinese Statistical Association
Organizer(s): Qi Tang, AbbVie
Chair(s): Richard A. Rode, AbbVie
8:35 AM Use of the VG (Virtual Twins Combined with GUIDE) Method in the Development of Precision Medicines Jia Jia ; Qi Tang, AbbVie ; Wangang Xie, AbbVie ; Richard A. Rode, AbbVie
8:55 AM Comparison of Some Subgroup Identification Algorithms for Precision Medicine in Drug Development Xin Huang ; Yan Sun, AbbVie ; Saptarshi Chatterjee, AbbVie ; Viswanath Devanarayan, AbbVie
9:15 AM Subgroup Identification Based on Multiple Outcomes Chensheng Kuang, University of Wisconsin - Madison ; Menggang Yu, University of Wisconsin - Madison ; Sijian Wang, University of Wisconsin - Madison
9:35 AM Development of Predictive Signature to Identify Patient Subgroups with Differential Treatment Effect Yu-Chuan Chen ; James Chen, FDA/NCTR ; Un Jung Lee, FDA/NCTR
9:55 AM Subgroup Identification in a Learn-and-Confirm Setting Lei Shen, Eli Lilly and Company
10:15 AM Floor Discussion
 
 

655
Thu, 8/4/2016, 8:30 AM - 10:20 AM CC-W178b
New Advances in Clustering Algorithms — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Mary Akinyemi, University of Lagos
8:35 AM On Assessing the Difficulty of a Clustering Problem: The Introduction of Sensitivity and Specificity to Cluster Analysis Jonathon O'Brien
8:50 AM A Contribution to Distance-Based Clustering in the Presence of Errors Maha Bakoben, Imperial College London ; Tony Bellotti, Imperial College London ; Niall Adams, Imperial College London
9:05 AM An Adaptive Association Test for Microbiome Data Chong Wu
9:20 AM Model-Based Clustering with Measurement Errors Wanli Zhang, Oregon State University ; Yanming Di, Oregon State University
9:35 AM A Simultaneous Variable Selection and Clustering Method for High-Dimensional Multinomial Regression Model Sheng Ren, University of Cincinnati ; Jason Lu, Cincinnati Children's Hospital Research Foundation ; Emily Lei Kang, University of Cincinnati
9:50 AM A Bootstrap Procedure for Classification Problems Based on Features and Observations Resampling Junhong Liu, The University of Hong Kong
10:05 AM Floor Discussion
 
 

656
Thu, 8/4/2016, 8:30 AM - 10:20 PM CC-W182
Hypothesis Testing for Correlation and Dependence — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jongphil Kim, Moffitt Cancer Center
8:35 AM Dependence Discovery from Multimodal Data via Multiscale Graph Correlation Cencheng Shen, Temple University ; Carey Priebe, The Johns Hopkins University ; Joshua Vogelstein, The Johns Hopkins University ; Mauro Maggioni, Duke University
8:50 AM A Nonparametric Test of Independence Between Two Variables Bin Li, Louisiana State University ; Qingzhao Yu, Louisiana State University Health Sciences Center
9:05 AM Testing Statistical Significance of Canonical Correlation Coefficients Yunjin Choi, Stanford University ; Robert Tibshirani, Stanford University ; Jonathan Taylor, Stanford University
9:35 AM Sharp Computational-Statistical Phase Transitions via Oracle Computational Model Zhaoran Wang, Princeton ; Quanquan Gu, University of Virginia ; Han Liu, Princeton
9:50 AM Hypothesis Tests for Hypervolumes Under K-Dimensional ROC Manifold Rajarshi Dey, University of South Alabama
10:05 AM Edgeworth Expansion for Summation of Symmetric Statistics and Its Application in Analyzing Massive Data Liuhua Peng, Iowa State University ; Song Xi Chen, Peking University/Iowa State University
 
 

677 * !
Thu, 8/4/2016, 10:30 AM - 12:20 PM CC-W175a
The Good, the Bad, and the Messy: Innovations in Analysis of Electronic Medical Records — Invited Papers
Section on Statistical Learning and Data Science , Committee on Applied Statisticians
Organizer(s): Ruth Etzioni, Fred Hutchinson Cancer Research Center, Suchi Saria, The Johns Hopkins University
Chair(s): Frank Yoon, Mathematica Policy Research
10:35 AM Missing Data as a Causal and Probabilistic Problem Ilya Shpitser, Johns Hopkins Computer Science
11:00 AM Individualizing Prognosis of Disease Trajectories Using Longitudinal Electronic Medical Record Data: Application to Scleroderma Suchi Saria, The Johns Hopkins University ; Peter Schulam, The Johns Hopkins University
11:25 AM New Machine-Learning Approaches to Causal Inference Cynthia Rudin, Duke University
11:50 AM Discussant: Ruth Etzioni, Fred Hutchinson Cancer Research Center
12:10 PM Floor Discussion
 
 

697
Thu, 8/4/2016, 10:30 AM - 12:20 PM CC-W175b
Model Selection in High Dimensions: Theory and Inference — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Oleg Melnikov, Rice University
10:35 AM Consistency of Penalized Cross-Validation for Model Selection Jieyi Jiang, The Ohio State University ; Steven N. MacEachern, The Ohio State University ; Yoonkyung Lee, The Ohio State University
10:50 AM High-Dimensional Multivariate Repeated Measures Analysis with Unequal Covariance Matrices Xiaoli Kong, University of Kentucky ; Solomon W. Harrar, University of Kentucky
11:05 AM Model Selection Confidence Sets by Likelihood Ratio Testing Chao Zheng ; Davide Ferrari, University of Melbourne ; Yuhong Yang, University of Minnesota
11:20 AM Post-Selection Inference for E-MS Algorithm Gang Xu, University of Miami ; J. Sunil Rao, University of Miami
11:35 AM Sparse Clustering of High-Dimensional Gaussian Mixtures Jing Ma, University of Pennsylvania ; Tony Cai, University of Pennsylvania ; Linjun Zhang, University of Pennsylvania
11:50 AM High-Dimensional Matrix-Variate Linear Discriminant Analysis Aaron Molstad, University of Minnesota ; Adam Rothman, University of Minnesota
12:05 PM Pathwise Coordinate Optimization for Nonconvex Sparse Learning: Algorithm and Theory Tuo Zhao, The Johns Hopkins University ; Han Liu, Princeton ; Tong Zhang, Rutgers University
 
 
 
 
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