Name: 2019 Joint Statistical Meetings
Start: 2019-07-27T07:00:00+00:00
End: 2019-08-01
Location: Colorado Convention Center

Legend:
CC = Colorado Convention Center H = Hyatt Regency Denver at Colorado Convention Center
* = applied session ! = JSM meeting theme

Activity Details

219010	Sun, 7/28/2019, 7:30 AM - 3:30 PM	H-Capitol Ballroom 2
NISS Writing Workshop Day 1 — Other ICW
NISS/ASA Writing Workshop, ENAR, International Chinese Statistical Association, International Indian Statistical Association, IMS, Korean International Statistical Society
Organizer(s): Randy Freret, NISS

218926	Sun, 7/28/2019, 2:00 PM - 5:00 PM	H-Marble Boardroom
IMS Executive Committee Meeting (Closed) — Other ICW
IMS
Organizer(s): Elyse Gustafson, IMS

3	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-710
Recent Developments in Network Testing — Invited Papers
IMS
Organizer(s): Yi Yu, University of Bristol
Chair(s): Zongming Ma, University of Pennsylvania
2:05 PM	A Full-Rank Spectral Algorithm for Graph Matching Presentation Zhou Fan, Yale University; Cheng Mao, Yale University; Jiaming Xu, Duke Fuqua School of Business; Yihong Wu, Yale University
2:35 PM	Matrix Means for Network Estimation with Applications to fMRI Data Presentation Keith Levin, University of Michigan; Asad Lodhia, University of Michigan; Elizaveta Levina, University of Michigan
3:05 PM	Change Point Detection for Self-Exciting Point Processes Presentation Daren Wang, University of Chicago; Rebecca Willett, University of Chicago
3:35 PM	Floor Discussion

4 * !	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-112
Recent Advance of Causal Inference in Failure Time Settings — Invited Papers
ENAR, Biometrics Section, IMS
Organizer(s): Shu Yang, North Carolina State University
Chair(s): Linbo Wang, University of Toronto
2:05 PM	Semiparametric Estimation of Continuous-Time Structural Failure Time Model Presentation Shu Yang, North Carolina State University
2:30 PM	The Choice to Define Competing Risk Events as Censoring Events and Implications for Causal Inference Presentation Jessica Gerald Young, Harvard Medical School; Mats Julius Stensrud, Harvard School of Public Health; Eric Tchetgen Tchetgen, University of Pennsylvania; Miguel Hernan, Harvard University
2:55 PM	Marginal Structural Models for a Continuous Outcome When the Risk of Death Depends on Treatment Presentation Judith Lok, Boston University, Dept of Mathematics and Statistics
3:20 PM	Discussant: Daniel Scharfstein, Johns Hopkins School of Hygiene & Public Health
3:40 PM	Floor Discussion

5	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-607
New Developments in Modern Statistical Theory — Invited Papers
IMS
Organizer(s): Bodhisattva Sen, Columbia University
Chair(s): Bodhisattva Sen, Columbia University
2:05 PM	On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors Rohit Kumar Patra, University of Florida
2:35 PM	Towards Demystifying Over-Parameterization in Deep Learning Mahdi Soltanolkotabi, University of Southern California
3:05 PM	Empirical Optimal Transport: Inference, Algorithms, Applications Presentation Axel Munk, Inst. for Mathematical Stochastics, Göttingen University
3:35 PM	Floor Discussion

7	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-709
Fiber Bundles in Statistical Inference and Probability — Invited Papers
IMS, Statistical and Applied Mathematical Sciences Institute
Organizer(s): Sayan Mukherjee, Duke University
Chair(s): Sayan Mukherjee, Duke University
2:05 PM	A Statistical Pipeline for Feature Selection and Association Mapping with 3D Shapes Lorin Crawford, Brown University
2:30 PM	Irreducible Representations and Multi-Frequency Phase Synchronization Presentation Tingran Gao, University of Chicago; Zhizhen Zhao, University of Illinois at Urbana-Champaign
2:55 PM	Gibbs Posterior Consistency and the Thermodynamic Formalism Presentation Kevin McGoff, UNC Charlotte; Andrew B Nobel, University of North Carolina at Chapel Hill; Sayan Mukherjee, Duke University
3:20 PM	Floor Discussion

29	Sun, 7/28/2019, 2:00 PM - 3:50 PM	CC-502
SPEED: Survey Methods, Transportation Studies, SocioEconomics, and General Statistical Methods Part 1 — Contributed Speed
Survey Research Methods Section, Transportation Statistics Interest Group, Quality and Productivity Section, Business and Economic Statistics Section, IMS
Chair(s): Georgiy Bobashev, Research Triangle Institute
Poster Presentations for this session.
2:05 PM	Frame Development and Sample Design for the 2018 National Survey of Children's Health Presentation Emilee Sizemore, US Census Bureau; Tracy Mattingly, US Census Bureau; Antoinette Lubich, US Census Bureau
2:10 PM	A Modeling Approach to Compensate for Nonresponse and Selection Bias in Surveys Presentation Tien-Huan Lin, Westat; Ismael Flores Cervantes, Westat
2:15 PM	A Comparison of Clustering Criteria for Evaluating Multivariate Stratifications of Primary Sampling Units Presentation Padraic Murphy, U.S. Census Bureau
2:20 PM	Statistical Data Integration and Inference via Multilevel Regression and Poststratification Presentation Yajuan Si, University of Michigan
2:30 PM	Comparing the Performance of Machine Learning and Semiparametric Regression Methods for Prediction of Travel Times and Flows on Urban Mass Transit Systems Daniel Graham, Imperial College London
2:35 PM	The Relationship Between Driver Performance and Driver Workload Using Functional Data Analysis Presentation Jundi Liu, University of Washington; Erika Miller, Colorado State University; Linda Ng Boyle, University of Washington
2:40 PM	Causal Impacts of New Urban Transit Provision on Air Quality: a Case Study of Jubilee Line Extension in London Liang Ma, Imperial College London; Marc E. J. Stettler, Imperial College London; Daniel Graham, Imperial College London
2:45 PM	Comparing the Quality of Online to Interviewer-Gathered Survey Data: Preliminary Results from the 2019 Survey of Consumer Finances Web Experiment Presentation Richard Windle, Federal Reserve Board
2:50 PM	Cluster-Stratified Outcome-Dependent Sampling in Resource-Limited Settings: Inference and Small-Sample Considerations Presentation Sara Sauer, Harvard School of Public Health; Bethany Hedt-Gauthier, Harvard Medical School; Claudia Rivera-Rodriguez, University of Auckland; Sebastien Haneuse, Harvard T.H. Chan School of Public Health
3:00 PM	Bayesian Uncertainty Estimation Under Complex Sampling Presentation Matthew Williams, National Science Foundation; Terrance Savitsky, Bureau of Labor Statistics
3:05 PM	How Hard Is it to Remove Mode Effects in Multimode Surveys? Basic Weighting V. Three Model-Based Methods Matt Jans; Randy ZuWallack, ICF; Kelly Martin, ICF; Thomas Brassell, ICF; James Dayton, ICF; Stephen Immerwahr, NYC DOHMH; Amber Levanon Seligson, NYC DOHMH; Sahnah Lim, NYU
3:15 PM	Use of an Artificial Realistic Dataset to Compare the Performance of Different Cross-Sectional Methods for Estimating Crash Modification Factors Presentation Bo Lan, University of North Carolina; Raghavan Srinivasan, University of North Carolina Highway Safety Research Center
3:20 PM	Use of Matching Algorithms to Determine Unit Eligibility Presentation 1 Presentation 2 Brandon Hopkins, RTI International; Kimberly Ault, RTI International
3:25 PM	DOE Optimization of Managing Trip in Europe Presentation 1 Presentation 2 Charles Chen, Applied Materials; Mason Chen, Mission San Jose High School, Stanford OHS; Brianna Zheng, Basis School
3:30 PM	Does Location Matter? a Case-Study of the Influence of Geography in Measurement of Gasoline Price Inflation Presentation David Popko, Bureau of Labor Statistics; Ilmo Sung., U.S. Bureau of Labor Statistics
3:35 PM	Estimating Generalized Linear Models with the Pseudo-Marginal Metropolis-Hastings Algorithm Presentation Taylor Brown, University of Virginia; Tim McMurry, University of Virginia School of Medicine
3:40 PM	Two-Step Estimation for Time Varying ARCH Models Presentation Yuanyuan Zhang; Rong Liu, University of Toledo; Qin Shao, University of Toledo; Lijian Yang, Tsinghua University
3:45 PM	Floor Discussion

45 !	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-709
Emerging Methods for Network Testing and Related Problems — Invited Papers
IMS, Section on Statistical Learning and Data Science, Section on Statistics in Defense and National Security
Organizer(s): Eric Kolaczyk, Boston University
Chair(s): Elizabeth Upton, Boston University
4:05 PM	Goodness-of-Fit Tests for 3 Variants of the Stochastic Block Model Presentation Vishesh Karwa, Temple University; Debdeep Pati, Texas A&M University; Sonja Petrovic, Illinois Institute of Technology; Liam Solus, KTH, Sweden; Mateja Raic, University of Illinois at Chicago; Dane Wilburne, ICERM, Brown University; Nikita Alexeev, unknown; Robert Williams, Texas A&M University; Bowei Yan, University of Texas
4:30 PM	A Broad Perspective on Network Testing Sofia C Olhede, University College London; Patrick J Wolfe, Purdue University
4:55 PM	Signal Detection in Spiked Random Matrix and Network Models Zongming Ma, University of Pennsylvania; Debapratim Banerjee, University of Pennsylvania
5:20 PM	Discussant: Daniel L Sussman, Boston University
5:40 PM	Floor Discussion

53 * !	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-607
Medallion Lecture I — Invited Papers
IMS
Organizer(s): Rajen D Shah, University of Cambridge
Chair(s): Steve MacEachern, The Ohio State University
4:05 PM	On Statistical Thinking in Deep Learning Yee Whye Teh, University of Oxford
5:45 PM	Floor Discussion

75	Sun, 7/28/2019, 4:00 PM - 5:50 PM	CC-710
Probability and Statistics — Contributed Papers
IMS
Chair(s): Mohamad Kazem Shirani Faradonbeh, University of Florida
4:05 PM	Conditions on Identifiability of Finite Mixtures of Truncated Poisson Distributions Presentation Mozhdeh Forghani, University of Northern Colorado; Khalil Shafie, University of Northern Colorado
4:20 PM	A New Approach to the Expected Euler Characteristic Presentation Khalil Shafie, University of Northern Colorado
4:35 PM	Frequentist Inference Without Repeated Sampling Presentation 1 Presentation 2 Paul Vos, East Carolina University
4:50 PM	Estimation in the Popularity Adjusted Block Model Presentation Ramchandra Rimal, Univ. of Central Florida; Marianna Pensky, University of Central Florida
5:05 PM	Cross-Validation Nonparametric Bootstrap Study of the Linhart-Volkers-Zucchini Out-Of-Sample Prediction Error Formula for Logistic Regression Modeling Presentation Richard Golden, University of Texas At Dallas; Shaurabh Nandy, Foxbat Research; Vishal Patel, Foxbat Research
5:20 PM	Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting Haoyu Chen, North Carolina State University; Wenbin Lu, North Carolina State University; Rui Song, North Carolina State University
5:35 PM	Floor Discussion

89	Sun, 7/28/2019, 5:05 PM - 5:50 PM	CC-Hall C
SPEED: Survey Methods, Transportation Studies, SocioEconomics, and General Statistical Methods Part 2 — Contributed Poster Presentations
Survey Research Methods Section, Transportation Statistics Interest Group, Quality and Productivity Section, Business and Economic Statistics Section, IMS
Chair(s): Georgiy Bobashev, Research Triangle Institute
Oral Presentations for this session.
20:	Frame Development and Sample Design for the 2018 National Survey of Children's Health Emilee Sizemore, US Census Bureau; Tracy Mattingly, US Census Bureau; Antoinette Lubich, US Census Bureau
21:	A Modeling Approach to Compensate for Nonresponse and Selection Bias in Surveys Tien-Huan Lin, Westat; Ismael Flores Cervantes, Westat
22:	A Comparison of Clustering Criteria for Evaluating Multivariate Stratifications of Primary Sampling Units Padraic Murphy, U.S. Census Bureau
23:	Statistical Data Integration and Inference via Multilevel Regression and Poststratification Yajuan Si, University of Michigan
25:	Comparing the Performance of Machine Learning and Semiparametric Regression Methods for Prediction of Travel Times and Flows on Urban Mass Transit Systems Daniel Graham, Imperial College London
26:	The Relationship Between Driver Performance and Driver Workload Using Functional Data Analysis Jundi Liu, University of Washington; Erika Miller, Colorado State University; Linda Ng Boyle, University of Washington
27:	Causal Impacts of New Urban Transit Provision on Air Quality: a Case Study of Jubilee Line Extension in London Liang Ma, Imperial College London; Marc E. J. Stettler, Imperial College London; Daniel Graham, Imperial College London
28:	Comparing the Quality of Online to Interviewer-Gathered Survey Data: Preliminary Results from the 2019 Survey of Consumer Finances Web Experiment Richard Windle, Federal Reserve Board
29:	Cluster-Stratified Outcome-Dependent Sampling in Resource-Limited Settings: Inference and Small-Sample Considerations Sara Sauer, Harvard School of Public Health; Bethany Hedt-Gauthier, Harvard Medical School; Claudia Rivera-Rodriguez, University of Auckland; Sebastien Haneuse, Harvard T.H. Chan School of Public Health
30:	Bayesian Uncertainty Estimation Under Complex Sampling Matthew Williams, National Science Foundation; Terrance Savitsky, Bureau of Labor Statistics
31:	How Hard Is it to Remove Mode Effects in Multimode Surveys? Basic Weighting V. Three Model-Based Methods Matt Jans; Randy ZuWallack, ICF; Kelly Martin, ICF; Thomas Brassell, ICF; James Dayton, ICF; Stephen Immerwahr, NYC DOHMH; Amber Levanon Seligson, NYC DOHMH; Sahnah Lim, NYU
33:	Use of Matching Algorithms to Determine Unit Eligibility Brandon Hopkins, RTI International; Kimberly Ault, RTI International
34:	Use of an Artificial Realistic Dataset to Compare the Performance of Different Cross-Sectional Methods for Estimating Crash Modification Factors Bo Lan, University of North Carolina; Raghavan Srinivasan, University of North Carolina Highway Safety Research Center
35:	Does Location Matter? a Case-Study of the Influence of Geography in Measurement of Gasoline Price Inflation David Popko, Bureau of Labor Statistics; Ilmo Sung., U.S. Bureau of Labor Statistics
36:	DOE Optimization of Managing Trip in Europe Charles Chen, Applied Materials; Mason Chen, Mission San Jose High School, Stanford OHS; Brianna Zheng, Basis School
37:	Estimating Generalized Linear Models with the Pseudo-Marginal Metropolis-Hastings Algorithm Taylor Brown, University of Virginia; Tim McMurry, University of Virginia School of Medicine
38:	Two-Step Estimation for Time Varying ARCH Models Yuanyuan Zhang; Rong Liu, University of Toledo; Qin Shao, University of Toledo; Lijian Yang, Tsinghua University
Oral Presentations for this session.

99 * !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-111
Causal Inference with Non-Traditional Designs — Invited Papers
IMS, Section on Statistics in Epidemiology, American Public Health Association
Organizer(s): Maya B Mathur, Harvard University
Chair(s): Maya B Mathur, Harvard University
8:35 AM	Propensity Score Methods for Merging Observational and Experimental Data Sets Presentation Evan Rosenman, Stanford University; Art Owen, Stanford University; Michael Baiocchi, Stanford University; Hailey Banack, University at Buffalo
9:00 AM	The Trend-In-Trend Research Design for Causal Inference Presentation Ashkan Ertefaie, University of Rochester; Dylan Small, University of Pennsylvania; Sean Hennessy, University of Pennsylvania; Xinyao Ji, University of Pennsylvania; Charles Leonard, University of Pennsylvania
9:25 AM	Design and Analysis of Two-Stage Randomized Experiments Presentation Kosuke Imai, Harvard University; Zhichao Jiang, Harvard University
9:50 AM	Discussant: Dylan Small, University of Pennsylvania
10:15 AM	Floor Discussion

103 !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-205
New Developments on Statistical Machine Learning — Invited Papers
IMS, Section on Statistical Learning and Data Science, International Chinese Statistical Association
Organizer(s): Jianqing Fan, Princeton Univeristy
Chair(s): Yingying Fan, University of Southern California
8:35 AM	Deep Knockoffs Machines Presentation Emmanuel Candes, Stanford University; Yaniv Romano, Stanford University; Matteo Sesia, Stanford University
9:00 AM	Statistical and Computational Guarantees of EM with Random Initialization Harrison H. Zhou, Yale Uinversity ; Yihong Wu, Yale University
9:25 AM	Single-Index Thresholding in Quantile Regression Presentation Huixia Judy Wang, The George Washington University; Yingying Zhang, Fudan University; Zhongyi Zhu, Fudan University
9:50 AM	Transfer Learning for Nonparametric Classification T. Tony Cai, The Wharton School, University of Pennsylvania
10:15 AM	Floor Discussion

105 * !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-207
Medallion Lecture II — Invited Papers
IMS
Organizer(s): Rajen D Shah, University of Cambridge
Chair(s): Marina Vannucci, Rice University
8:35 AM	Learning and Exploiting Low-Dimensional Structure in High-Dimensional Data Presentation David Dunson, Duke University
10:15 AM	Floor Discussion

107 !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-203
The ABC of Making an Impact — Invited Papers
Section on Bayesian Statistical Science, International Society for Bayesian Analysis (ISBA), IMS
Organizer(s): Antonietta Mira, Università della Svizzera italiana and Università dell'Insubria
Chair(s): Christian Robert, Ceremade - Université Paris-Dauphine
8:35 AM	Simulated Annealing ABC (SABC) and Its Application to a Stochastic Solar Dynamo Model Presentation Carlo Albert, Swiss Federal Institute of Aquatic Science and Technology (Eawag)
9:05 AM	ABC and Forests: Where We Are and Where We Are Going Louis Raynal, Alexander Grothendieck Montpellier Institute, University of Montpellier; Alice Cleynen, Alexander Grothendieck Montpellier Institute, University of Montpellier; Jean-Michel Marin, Alexander Grothendieck Montpellier Institute, University of Montpellier
9:35 AM	Loss-Based Bayesian Prediction Presentation David Frazier, Monash University; Gael Martin, Monash University; Ruben Loaiza-Maya, Monash University
10:05 AM	Floor Discussion

108	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-704
Multivariate Extremes: Theory and Applications — Invited Papers
Section on Risk Analysis, IMS
Organizer(s): John P Nolan, American University
Chair(s): Aric LaBarr, Elder Research Inc.
8:35 AM	Testing the Multivariate Regular Variation Model Chen Zhou, Erasmus University Rotterdam
8:55 AM	Why Model the Growth of Networks? Presentation Sidney Ira Resnick, Cornell
9:15 AM	Semiparametric Estimation for Multivariate Extremes John P Nolan, American University; Anne-Laure Fougeres, Univesrity of Lyon; Cecile Mercadier, University of Lyon
9:35 AM	Multiple Testing and Extremes: Exact Signal Support Recovery in High Dimensions Presentation Zheng Gao, University of Michigan; Stilian Stoev, University of Michigan
9:55 AM	Modeling Extreme Wind Speeds Using Max-Infinitely Divisible Spatial Processes Raphaël Huser, King Abdullah University of Science and Technology; Thomas Opitz, INRA; Emeric Thibaud, EPFL
10:15 AM	Floor Discussion

123 * !	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-301
New Challenges and Opportunities in Nonparametric Statistics — Topic Contributed Papers
Section on Nonparametric Statistics, IMS, International Chinese Statistical Association
Organizer(s): Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
Chair(s): Derek Young, University of Kentucky
8:35 AM	High-Dimensional Robust Covariance Matrix Estimation for Compositional Microbiome Data Arun Srinivasan, Pennsylvania State University; Lingzhou Xue, Penn State University and National Institute of Statistical Sciences; Xiang Zhan, Penn State University
8:55 AM	Two Sample High-Dimensional Covariance Test Danning Li, Penn State University; Lingzhou Xue, Penn State University and National Institute of Statistical Sciences; Xiufan Yu, Penn State University
9:15 AM	A General Framework for Sparse Sufficient Dimension Reduction Presentation Wei Luo, Zhejiang University
9:35 AM	On Dual Model-Free Variable Selection with Two Groups of Variables Yuexiao Dong, Temple University; Ahmad Alothman, Kuwait University; Andreas Artemiou, Cardiff University
9:55 AM	Temporal Exponential-Family Random Graph Models with Time-Evolving Latent Block Structure for Dynamic Networks Kevin Lee, Western Michigan University; Amal Agarwal, The Pennsylvania State University; Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
10:15 AM	Floor Discussion

132	Mon, 7/29/2019, 8:30 AM - 10:20 AM	CC-210/212
Functional Data and Time Series — Contributed Papers
IMS
Chair(s): Ruiyan Luo, Georgia State University
8:35 AM	Estimation and Inference for Functional Linear Regression Models with Varying Regression Coefficients Guanqun Cao, Auburn University; Li Wang, Iowa State University; Shuoyang Wang, Auburn University
8:50 AM	Robust M-Estimation for Partially Observed Functional Data Yeonjoo Park, University of Texas at San Antonio; Xiaohui Chen, University of Illinois at Urbana-Champaign; Douglas Simpson, University of Illinois at Urbana-Champaign
9:05 AM	Detecting Linear Trend Changes and Point Anomalies in Data Sequences Hyeyoung Maeng, London School of Economics; Piotr Fryzlewicz, London School of Economics
9:20 AM	Two-Sample Mean Tests for High-Dimensional Time Series Data Shuyi Zhang, Peking University; Yumou Qiu, Iowa State University; Song Xi Chen, Peking University
9:35 AM	On Some Estimation and Testing Problems for Distribution Functions Under Dependence Presentation Sucharita Ghosh, Swiss Federal Research Institute WSL
9:50 AM	Functional Autoregressive Model Using Signal Compression Presentation Husneara Rahman, Georgia State University; Xin Qi, Georgia State University
10:05 AM	Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Time Series Seyed Yaser Samadi, Southern Illinois University, Carbondale; Priyan Alwis, Southern Illinois University, Carbondale

145 !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-704
Causal Inference — Invited Papers
IMS
Organizer(s): Peter Bühlmann, ETH Zurich
Chair(s): Alberto Roverato, University of Padua
10:35 AM	Bracketing in the Comparative Interrupted Time-Series Design to Address Concerns About History Interacting with Group: Evaluating Missouri’s Handgun Purchaser Law Presentation Raiden Hasegawa, University of Pennsylvania; Daniel Webster, Johns Hopkins University; Dylan Small, University of Pennsylvania
11:05 AM	Anchor Regression: Heterogeneous Data Meets Causality Dominik Rothenhäusler, UC Berkeley; Nicolai Meinshausen, ETH Zürich; Peter Bühlmann, ETH Zurich; Jonas Peters, University of Copenhagen
11:35 AM	Rerandomization and ANCOVA Presentation Peng Ding, University of California, Berkeley; Xinran Li, Wharton Statistics
12:05 PM	Floor Discussion

146	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-605
Scaling up Bayesian Inference for Massive Data Sets — Invited Papers
IMS, International Society for Bayesian Analysis (ISBA), Section on Bayesian Statistical Science
Organizer(s): Trevor Campbell, University of British Columbia; David Dunson, Duke University; Jonathan Huggins, Harvard University
Chair(s): Jonathan Huggins, Harvard University
10:35 AM	Continuous-Time Monte Carlo and Scalable Bayesian Inference Presentation Paul Fearnhead, Lancaster University
11:00 AM	Scalable Gaussian Process Inference with Finite-Data Mean and Variance Guarantees Presentation Tamara Broderick, Massachusetts Institute of Technology
11:25 AM	Gaussian Variational Approximation for High-Dimensional State Space Models Presentation Robert Kohn, University of New South Wales
11:50 AM	Some Applications of Approximate MCMC Presentation Anirban Bhattacharya, TAMU
12:15 PM	Floor Discussion

147 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-Four Seasons 1
Wald Lecture I — Invited Papers
IMS
Organizer(s): Piotr Fryzlewicz, London School of Economics
Chair(s): Robert Tibshirani, Stanford University
10:35 AM	Wald I: Statistical Learning with Sparsity Presentation 1 Presentation 2 Trevor J. Hastie, Stanford University
12:15 PM	Floor Discussion

150 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-603
Recent Advances in Nonparametric Statistical Methods for Complex Data — Invited Papers
Section on Nonparametric Statistics, IMS, Section on Statistical Learning and Data Science
Organizer(s): Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
Chair(s): Danning Li, Penn State University
10:35 AM	Statistical Approach to Topological Data Analysis Kenji Fukumizu, Institute of Statistical Mathematics
11:00 AM	Dimension Reduction for Functional Databased on Weak Conditional Moments Presentation Bing Li, The Pennsylvania State University; Jun Song, University of North Carolina at Charlotte
11:25 AM	Nonconvex Statistical Learning for the Dimensionality Reduction of High-Dimensional Data Lingzhou Xue, Penn State University and National Institute of Statistical Sciences; Shiqian Ma, University of California, Davis; Hui Zou, University of Minnesota
11:50 AM	Detecting Rare and Weak Spikes in Large Covariance Matrices Zheng Tracy Ke, Harvard University
12:15 PM	Floor Discussion

163 * !	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-708
Methods for Complex Data: The Next Generation — Topic Contributed Papers
Business and Economic Statistics Section, Section on Statistical Learning and Data Science, Business Analytics/Statistics Education Interest Group, IMS
Organizer(s): David Matteson, Cornell University
Chair(s): Ines Wilms, Maastricht University
10:35 AM	Structured Shrinkage Priors Presentation Maryclare Griffin, Cornell University Center for Applied Mathematics; Peter Hoff, Duke University
10:55 AM	High-Dimensional Causal Discovery with Non-Gaussian Data Presentation Y. Samuel Wang, University of Chicago; Mathias Drton, University of Washington
11:15 AM	Projection pursuit based generalized betas accounting for higher order co-moment effects in financial market analysis Presentation Sven Serneels, Aspen Technology
11:35 AM	Learning Local Dependence in Ordered Data Guo Yu, University of Washington; Jacob Bien, University of Southern California
11:55 AM	Sequential Change-Point Detection for High-Dimensional and Non-Euclidean Data Lynna Chu, University of California, Davis; Hao Chen, University of California, Davis
12:15 PM	Floor Discussion

185	Mon, 7/29/2019, 10:30 AM - 12:20 PM	CC-Hall C
Contributed Poster Presentations: IMS — Contributed Poster Presentations
IMS
Chair(s): Wendy Meiring, University of California At Santa Barbara
5:	Finite Mixture Regression Models for Stratified Sample Abdelbaset Abdalla, South Dakota State University; Semhar Michael, South Dakota State University
6:	Relative Accuracy of Multivariate Bootstrap Procedures Dewei Zhong, 1992; John E Kolassa, Rutgers, the State University of New Jersey
7:	Multiple Hypothesis Testing with Discrete Data: Minimally Discrete P-Values Joshua Habiger, Oklahoma State University

218927	Mon, 7/29/2019, 12:30 PM - 2:00 PM	H-Mineral Hall A
IMS Editors Meetings — Other ICW
IMS
Organizer(s): Elyse Gustafson, IMS

218928	Mon, 7/29/2019, 12:30 PM - 2:00 PM	H-Mineral Hall C
Annals of Statistics Editors Meeting — Other ICW
IMS
Organizer(s): Elyse Gustafson, IMS

213 * !	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-104
Sequential Decision Making and Causal Inference — Invited Papers
IMS, ENAR, Institute for Operations Research and the Management Sciences
Organizer(s): Susan Murphy, Harvard University
Chair(s): Susan Murphy, Harvard University
2:05 PM	Mostly Exploration-Free Algorithms for Contextual Bandits Mohsen Bayati, Stanford University
2:30 PM	Truncated Thompson Sampling for Safe and Efficient Precision Public Health Presentation Eric B Laber, NC State University; Jesse Clifton, NC State University
2:55 PM	Learning to Personalize from Observational Data Under Unobserved Confounding Nathan Kallus, Cornell University and Cornell Tech
3:20 PM	Discussant: Elizabeth Ginexi, National Institutes of Health
3:40 PM	Floor Discussion

215 !	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-603
Evolving Survey Inference in the Big Data Era: Challenges and Opportunities — Invited Papers
Survey Research Methods Section, Government Statistics Section, IMS
Organizer(s): Yajuan Si, University of Michigan
Chair(s): Yajuan Si, University of Michigan
2:05 PM	Small Area Estimation to Correct for Measurement Errors in Big Population Registers Presentation Dano Ben-Hur, Central Bureau of Statistics, Israel; Danny Pfeffermann, Central Bureau of Statistics and Hebrew Unversity, Israel, University of Southampton, UK
2:30 PM	Revisiting Design-Based Inference Presentation Jean Opsomer, Westat
2:55 PM	Novel Methods for Incorporating Sample Designs in Bayesian Inference Presentation Michael Elliott, University of Michigan; Yuqi Zhai, University of Michigan; Trivellore Raghunathan, University of Michigan
3:20 PM	Combining Non-Probability and Probability Survey Samples Through Mass Imputation Presentation Jae-kwang Kim, Iowa State University; Seho Park , Dartmouth University ; Yilin Chen, University of Waterloo; Changbao Wu, University of Waterloo
3:45 PM	Floor Discussion

218 * !	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-201
Medallion Lecture III — Invited Papers
IMS
Organizer(s): Rajen D Shah, University of Cambridge
Chair(s): Steve Marron , University of North Carolina at Chapel Hill
2:05 PM	Breaking Curse of Dimensionality in Nonparametrics Helen Zhang, University of Arizona
3:45 PM	Floor Discussion

220	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-102
Uncertainty Quantification for Stochastic Optimization Methods in Machine Learning — Invited Papers
IMS, IEEE Computer Society
Organizer(s): Weijie Su, University of Pennsylvania
Chair(s): Weijie Su, University of Pennsylvania
2:30 PM	Convergence Diagnostics for Stochastic Gradient Methods Presentation Panagiotis Toulis, University of Chicago Booth School of Business; Jerry Chee, University of Chicago
2:55 PM	Data-Adaptive Learning Rate Selection for Stochastic Gradient Descent Using Convergence Diagnostic Presentation 1 Presentation 2 Matteo Sordello, University of Pennsylvania; Weijie Su, University of Pennsylvania
3:20 PM	First-Order Newton-Type Estimator for Distributed Estimation and Inference Xi Chen, New York University; Weidong Liu, Shanghai Jiaotong University; Yichen Zhang, New York University
3:45 PM	Floor Discussion

243	Mon, 7/29/2019, 2:00 PM - 3:50 PM	CC-106
Functional Object Analysis and Beyond — Contributed Papers
IMS
Chair(s): Yining Chen, London School of Economics
2:05 PM	Wasserstein F-Tests and Confidence Bands for the Fréchet Regression of Density Response Curves Presentation Alexander Petersen, University of California, Santa Barbara; Xi Liu, University of California, Santa Barbara; Afshin Divani, University of Minnesota
2:20 PM	Efficient Multivariate Functional Estimation and the Super-Oracle Phenomenon Presentation Thomas Berrett, University of Cambridge; Richard Samworth, University of Cambridge
2:35 PM	Two-Component Mixture Model in the Presence of Covariates Presentation Nabarun Deb, Columbia University; Sujayam Saha, Google; Adityanand Guntuboyina, University of California at Berkeley; Bodhisattva Sen, Columbia University
2:50 PM	Optimal Estimation of Wasserstein Distance on a Tree with an Application to Microbiome Studies Shulei Wang, University of Pennsylvania; T. Tony Cai, The Wharton School, University of Pennsylvania; Hongzhe Li, University of Pennsylvania
3:05 PM	A Goodness of Fit Test for Object Data Using Nearest Neighbors Presentation Leif Ellingson, Texas Tech University; Dong Xu, Texas Tech University
3:20 PM	Nonparametric Estimation of Surface Integrals on Level Sets Wanli Qiao, George Mason University
3:35 PM	Edgeworth Expansions for Minimum Divergence Estimators Zhengyang Fan; Anand Vidyashankar, George Mason University

262 * !	Mon, 7/29/2019, 8:00 PM - 9:30 PM	CC-Four Seasons 1
IMS Presidential Address and Awards Ceremony — Invited Papers
IMS
Organizer(s): Piotr Fryzlewicz, London School of Economics
Chair(s): Alison Etheridge, University of Oxford
8:05 PM	011, 010111, and 011111100100 Presentation Xiao-Li Meng, Harvard University

218929	Mon, 7/29/2019, 9:30 PM - 11:00 PM	CC-Four Seasons 1
IMS Reception — Other ICW
IMS
Organizer(s): Elyse Gustafson, IMS

269 !	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-712
New Perspectives on Statistical Robustness — Invited Papers
IMS, International Indian Statistical Association, Section on Nonparametric Statistics
Organizer(s): Po-Ling Loh, UW-Madison
Chair(s): Po-Ling Loh, UW-Madison
8:35 AM	Learning Discrete Markov Random Fields with Nearly Optimal Runtime and Sample Complexity Adam Klivans, UT Austin
9:00 AM	Algorithmic Questions in High-Dimensional Robust Statistics Ilias Diakonikolas, USC
9:25 AM	Robust Learning: Information Theory and Algorithms Jacob Steinhardt, UC Berkeley
9:50 AM	Robust Estimation via Robust Gradient Estimation Pradeep Ravikumar, Carnegie Mellon University
10:15 AM	Floor Discussion

272	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-607
Statistical Learning for Complex and High-Dimensional Data — Invited Papers
IMS
Organizer(s): Tony Cai, University of Pennsylvania
Chair(s): Richard Samworth, University of Cambridge
8:35 AM	Estimation and Inversion of Generative Networks John Lafferty, Yale University
9:05 AM	Sparse Grid Meets Random Hashing: Learning High-Dimensional Functions of Few Variables Ming Yuan, Columbia University
9:35 AM	How to Deal with Big Data? Understanding Large-Scale Distributed Regression Edgar Dobriban, University of Pennsylvania; Yue Sheng, University of Pennsylvania
10:05 AM	Floor Discussion

288 !	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-705
New Insights from Classical Wisdom—honoring Lawrence D. Brown’s Contributions to Graduate Student Education — Topic Contributed Papers
IMS, Section on Teaching of Statistics in the Health Sciences
Organizer(s): Chaitra Nagaraja, Fordham University
Chair(s): Linda Zhao, University of Pennsylvania
8:35 AM	Randomness-Free Study of Smooth M-Estimators Presentation Arun Kuchibhotla, University of Pennsylvania
8:55 AM	REGRESSION ADJUSTMENT in COMPLETELY RANDOMIZED EXPERIMENTS with a DIVERGING NUMBER of COVARIATES Presentation Lihua Lei, UC Berkeley; Peng Ding, University of California, Berkeley
9:15 AM	Nonparametric Empirical Bayes Methods for Sparse, Noisy Signals Junhui Cai; Linda Zhao, University of Pennsylvania
9:35 AM	Testing for Independence with BERET Duyeol Lee, University of North Carolina at Chapel Hill; Kai Zhang, University of North Carolina, Chapel Hill; Michael Kosorok, University of North Carolina at Chapel Hill
9:55 AM	Discussant: Kai Zhang, University of North Carolina, Chapel Hill
10:15 AM	Floor Discussion

290 * !	Tue, 7/30/2019, 8:30 AM - 10:20 AM	CC-703
Big Data in Time Series and Spatial Data Analysis: Theory and Applications — Topic Contributed Papers
Royal Statistical Society, IMS, Section on Statistical Computing
Organizer(s): Sucharita Ghosh, Swiss Federal Research Institute WSL
Chair(s): Sucharita Ghosh, Swiss Federal Research Institute WSL
8:35 AM	Two Sample Testing for Multivariate Functional Data Presentation Klaus Telkmann, University of California Irvine; Dustin Pluta, University of California Irvine; Hernando Ombao, King Abdullah University of Science and Technology (KAUST); Babak Shahbaba, University of California Irvine
8:55 AM	Parameter Estimation for Big Data in Time Series and Random Fields Presentation Adam Sykulski, Lancaster University; Sofia C Olhede, University College London; Arthur Guillaumin, University College London
9:15 AM	Nonparametric Regression Under Semi-Long Range Dependence Farzad Sabzikar, Iowa State University
9:35 AM	Further Development of the Double Conditional Smoothing for Nonparametric Surfaces Under a Lattice Spatial Model Presentation Yuanhua Feng; Bastian Schäfer, Paderborn University
9:55 AM	Discussant: Jan Beran, University of Konstanz
10:15 AM	Floor Discussion

312 !	Tue, 7/30/2019, 10:30 AM - 12:20 PM	CC-203
Theory for Deep Neural Networks — Invited Papers
IMS
Organizer(s): Johannes Schmidt-Hieber, Leiden University
Chair(s): Johannes Schmidt-Hieber, Leiden University
10:35 AM	On Deep Learning as a Remedy for the Curse of Dimensionality in Nonparametric Regression Presentation Michael Kohler, Technische Universitaet Darmstadt; Sophie Langer, Technische Universitaet Darmstadt
11:05 AM	Robust Estimation and Generative Adversarial Nets Presentation Chao Gao, University of Chicago
11:35 AM	Generalization Analysis for Mechanism of Deep Learning via Nonparametric Statistics Masaaki Imaizumi, Institute of Statistical Mathematics
12:05 PM	Floor Discussion

318 * !	Tue, 7/30/2019, 10:30 AM - 12:20 PM	CC-207
Rietz Lecture — Invited Papers
IMS
Organizer(s): Rajen D Shah, University of Cambridge
Chair(s): T. Tony Cai, The Wharton School, University of Pennsylvania
10:35 AM	Selective Inference: The Silent Killer of Replicability Presentation Yoav Benjamini, Tel Aviv University
12:15 PM	Floor Discussion

345	Tue, 7/30/2019, 10:30 AM - 12:20 PM	CC-210/212
High-Dimensional Statistics — Contributed Papers
IMS
Chair(s): Lihua Lei, UC Berkeley
10:35 AM	Likelihood Ratio Test in Multivariate Linear Regression: From Low to High Dimension Yinqiu He, University of Michigan; Tiefeng Jiang, University of Minnesota; Jiyang Wen, Johns Hopkins University; Gongjun Xu, University of Michigan
10:50 AM	Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models Rong Ma, Univ of Pennsylvania; T. Tony Cai, The Wharton School, University of Pennsylvania; Hongzhe Li, University of Pennsylvania
11:05 AM	Inference for High-Dimensional Linear Mixed Effects Models: a Quasi-Likelihood Approach Sai Li, University of Pennsylvania; Hongzhe Li, University of Pennsylvania; T. Tony Cai, The Wharton School, University of Pennsylvania
11:20 AM	Divergence Based Inference for High-Dimensional GLMM Lei Li, George Mason University; Anand Vidyashankar, George Mason University
11:35 AM	Fundamental Limits of Exact Support Recovery in High Dimensions Zheng Gao, University of Michigan; Stilian Stoev, University of Michigan
11:50 AM	Debiased Inference in High-Dimensional Single-Index Models Under Gaussian Design Presentation Hamid Eftekhari, University of Michigan; Moulinath of Banerjee, university of michigan; Ya'acov Ritov, university of michigan
12:05 PM	Inference for Heterogeneous Quantile Treatment Effects in High Dimensions: Rank and Score Balancing Alexander Giessing, Princeton University; Jingshen Wang, University of Michigan

218930	Tue, 7/30/2019, 12:30 PM - 2:00 PM	H-Centennial Ballroom A
IMS Council and Business Meeting — Other ICW
IMS
Organizer(s): Elyse Gustafson, IMS

378 * !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-Four Seasons 1
Wald Lecture II — Invited Papers
IMS
Organizer(s): Piotr Fryzlewicz, London School of Economics
Chair(s): Xihong Lin, Harvard
2:05 PM	Wald II: Statistical Learning with Sparsity Presentation Trevor J. Hastie, Stanford University
3:05 PM	Discussant: Rahul Mazumder, MIT
3:25 PM	Discussant: William Fithian, University of California at Berkeley
3:45 PM	Floor Discussion

381 * !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-706
Recent Advances in Multiple Testing and False Discovery Rate Analysis — Invited Papers
IMS, International Chinese Statistical Association, International Statistical Institute
Organizer(s): Wenguang Sun, University of Southern California
Chair(s): Jacob Bien, University of Southern California
2:05 PM	A New Approach for Large-Scale Multiple Testing with Application to FDR Control for Graphically Structured Hypotheses Presentation Wenge Guo, New Jersey Institute of Technology; Gavin Lynch, Catchpoint Systems, Inc.; Joseph P. Romano, Stanford University
2:25 PM	Optimal False Discovery Rate Control in the Two-Group Model Presentation Ruth Heller, Tel-Aviv University; Saharon Rosset, Tel Aviv University
2:45 PM	SOAR: Structure Online--Adaptive Rules for False Discovery Rate Control in Dynamic Models Wenguang Sun, University of Southern California; Weinan Wang, Snap Inc.
3:05 PM	Closed Testing and Admissibility of Procedures Controlling False Discovery Proportions Presentation Jelle Goeman, Leiden University Medical Center; Jesse Hemerik, University of Oslo; Aldo Solari, University of Milano-Bicocca
3:25 PM	Adapting to One- and Two-Way Classified Structures of Hypotheses While Controlling False Discoveries Presentation Sanat K Sarkar, Temple University
3:45 PM	Floor Discussion

392 !	Tue, 7/30/2019, 2:00 PM - 3:50 PM	CC-705
Large-Scale Data Analysis via Spectral Methods — Topic Contributed Papers
IMS, Section on Statistical Learning and Data Science
Organizer(s): Edgar Dobriban, University of Pennsylvania
Chair(s): Edgar Dobriban, University of Pennsylvania
2:05 PM	Bootstrapping Spectral Statistics in High Dimensions Miles Lopes, UC Davis; Alexander Aue, University of California, Davis; Andrew Blandino, UC Davis
2:25 PM	Unsupervised Ensemble Learning: a Spectral Approach Boaz Nadler, Weizmann Institute of Science
2:45 PM	Distributed Ridge Regression in High Dimensions Yue Sheng, University of Pennsylvania; Edgar Dobriban, University of Pennsylvania
3:05 PM	"Spectral Algorithms for High-Dimensional Data Analysis: What Have We Learned" Matan Gavish, Hebrew Univ of Jerusalem
3:25 PM	Joint Behavior of Large Autocovariance Matrices Presentation Arup Bose, Indian Statistical Institute
3:45 PM	Floor Discussion

218931	Wed, 7/31/2019, 7:30 AM - 9:00 AM	H-Limestone Boardroom
Statistics Surveys Editors Meeting — Other ICW
IMS
Organizer(s): Elyse Gustafson, IMS

440 * !	Wed, 7/31/2019, 8:30 AM - 10:20 AM	CC-607
Medallion Lecture IV — Invited Papers
IMS
Organizer(s): Rajen D Shah, University of Cambridge
Chair(s): Eric Kolaczyk, Boston University
8:35 AM	Hierarchical Communities in Networks: Theory and Practice Presentation Elizaveta Levina, University of Michigan
10:15 AM	Floor Discussion

441	Wed, 7/31/2019, 8:30 AM - 10:20 AM	CC-504
Recent Advances in Nonparametric Statistics — Invited Papers
IMS
Organizer(s): Cun-Hui Zhang, Rutgers University
Chair(s): Cun-Hui Zhang, Rutgers University
8:35 AM	ISOTONIC REGRESSION in MULTI-DIMENSIONAL SPACES and GRAPHS Hang Deng, Rutgers University; Cun-Hui Zhang, Rutgers University
9:00 AM	Linear Classification and the Manski Model Presentation Ya'acov Ritov, university of michigan; Debarghya of Mukherjee, university of michigan; Moulinath of Banerjee, university of michigan
9:25 AM	Estimating Rectangular Piecewise Constant Functions in Multiple Dimensions Presentation Bodhisattva Sen, Columbia University; Adityanand Guntuboyina, University of California at Berkeley; Billy Fang, University of California at Berkeley
9:50 AM	Trend Filtering on Images Presentation Veeranjaneyulu Sadhanala, Carnegie Mellon; Yu-Xiang Wang, UC Santa Barbara; James Sharpnack, UC Davis; Ryan Tibshirani, Carnegie Mellon University
10:15 AM	Floor Discussion

455 !	Wed, 7/31/2019, 8:30 AM - 10:20 AM	CC-502
Recent Advances in Bayesian Computation: Theory and Methods — Topic Contributed Papers
IMS, International Society for Bayesian Analysis (ISBA), Section on Bayesian Statistical Science
Organizer(s): Vivekananda Roy, Iowa State University
Chair(s): Aixin Tan, University of Iowa
8:35 AM	Convergence Complexity Analysis of MCMC Algorithms James Hobert
8:55 AM	Weighted Batch Means Estimators in Markov Chain Monte Carlo Presentation James Flegal, University of California, Riverside
9:15 AM	Convergence Complexity of Gibbs Samplers for Bayesian Vector Autoregressive Processes Presentation Galin Jones, University of Minnesota; Karl Oskar Ekvall, University of Minnesota
9:35 AM	Recent Advances in Bayesian Computation: Theory and Methods Presentation Murali Haran, Penn State University; Jaewoo Park, Penn State University
9:55 AM	Bayesian Registration of Functions with a Gaussian Process Prior Radu Herbei, Ohio State University; Yi Lu, Drew University; Sebastian Kurtek, The Ohio State University
10:15 AM	Floor Discussion

477 !	Wed, 7/31/2019, 10:30 AM - 12:20 PM	CC-710
Complex Time Series Analysis — Invited Papers
IMS
Organizer(s): Qiwei Yao, London School of Economics
Chair(s): Rong Chen, Rutgers University
10:35 AM	Highly Comparative Time-Series Analysis as Statistical Learning Across a Massive Interdisciplinary Feature Library Presentation Ben David Fulcher, University of Sydney
11:00 AM	Testing for Trends in High-Dimensional Time Series Likai Chen, Washington University in Saint Louis; Wei Biao Wu, University of Chicago
11:25 AM	Multivariate Spatial-Temporal Prediction on Latent Low-Dimensional Functional Structure with Non-Stationarity YI CHEN, Princeton University; Qiwei Yao, London School of Economics; Rong Chen, Rutgers University
11:50 AM	High-Dimensional Change-Point Estimation with Heterogeneous Noise Yining Chen, London School of Economics
12:15 PM	Floor Discussion

481 !	Wed, 7/31/2019, 10:30 AM - 12:20 PM	CC-708
Random Matrices and High-Dimensional Statistics — Invited Papers
IMS
Organizer(s): Iain Johnstone, Stanford University
Chair(s): Iain Johnstone, Stanford University
10:35 AM	Large Random Matrices: Spiked Models, Stationnary Processes and Applications Jamal Najim, CNRS and Université Paris-Est
11:00 AM	Testing High-Dimensional Cointegration Presentation 1 Presentation 2 Alexei Onatski, Cambridge University
11:25 AM	Edge Statistics of Sparse Random Sample Covariance Matrices Presentation Kevin Schnelli, KTH Royal Institute of Technology
11:50 AM	Random Matrices and the Bootstrap in Moderate and High-Dimensions Presentation Noureddine El Karoui, Criteo AI Lab and UC, Berkeley; Elizabeth Purdom, UC, Berkeley
12:15 PM	Floor Discussion

484 * !	Wed, 7/31/2019, 10:30 AM - 12:20 PM	CC-Four Seasons 1
Wald Lecture III — Invited Papers
IMS
Organizer(s): Piotr Fryzlewicz, London School of Economics
Chair(s): Gareth James, University of Southern California
10:35 AM	Wald III: Statistical Learning with Sparsity Presentation Trevor J. Hastie, Stanford University
11:35 AM	Discussant: Ming Yuan, Columbia University
11:55 AM	Discussant: Hui Zou, University of Minnesota
12:15 PM	Floor Discussion

509	Wed, 7/31/2019, 10:30 AM - 12:20 PM	CC-712
Statistical Methodology — Contributed Papers
IMS
Chair(s): Rong Ma, Univ of Pennsylvania
10:35 AM	Covariate Assisted Principal Regression for Covariance Matrix Outcomes Yi Zhao, Johns Hopkins Bloomberg School of Public Health; Bingkai Wang, Johns Hopkins Bloomberg School of Public Health; Stewart Mostofsky, Johns Hopkins University; Brian Caffo, Johns Hopkins Bloomberg School of Public Health; Xi Luo, The University of Texas Health Science Center at Houston
10:50 AM	Integrating Multi-Source Block-Wise Missing Data in Model Selection Fei Xue, University of Illinois at Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
11:05 AM	Analysis of Variance Models Through Information Theory Chathurangi Heshani Pathiravasan, Southern Illinois University; Bhaskar Bhattacharya, Southern Illinois University
11:20 AM	Sample Size Calculations in Simple Linear Regression: Exact Approach Presentation Marepalli Rao, University of Cincinnati; Tianyuan B Guan, University of Cincinnati
11:35 AM	Covariance Based Moment Equations for Improved Variance Component Estimation Sanjay Chaudhuri, National University of Singapore
11:50 AM	Causality and Intervention in the Context of Stochastic Differential Equation Models Paromita Banerjee, Case Western Reserve University; Wojbor Woyczynski, Case Western Reserve University; Jeffrey M Albert, Case Western Reserve University
12:05 PM	Controlling False Discoveries with Confidence: a Theoretical Investigation in the Asymptotic Variance of the False Discovery Proportion Meng Mei, Oregon State University; Yuan Jiang, Oregon State University

545 * !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-607
Towards Perfect and Scalable Distributional Computation — Invited Papers
IMS, International Society for Bayesian Analysis (ISBA), Section on Statistical Computing
Organizer(s): Xiao-Li Meng, Harvard University
Chair(s): David Jones, Texas A&M University
2:05 PM	Exact Estimation with Markov Chain Monte Carlo Presentation Aguemon Yves Atchade, Boston University; Xiao-Li Meng, Harvard University
2:30 PM	The Never-Ending MCMC Revolution: Making Dempster-Shafer Modeling Practical Presentation Ruobin Gong, Rutgers University; Xiao-Li Meng, Harvard University
2:55 PM	Fiducial Selector: Scalable Statistical Inference for High-Dimensional Regression Problems Thomas C. M. Lee, UC Davis; Jan Hannig, UNC Chapel Hill; Randy Lai, U of Maine; Chunzhe Zhang, UC Davis
3:20 PM	Discussant: Keli Liu, Stanford University
3:45 PM	Floor Discussion

547	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-Four Seasons 1
Annals of Statistics Special Invited Session: Selected Papers — Invited Papers
IMS
Organizer(s): Edward George, University of Pennsylvania; Tailen Hsing, University of Michigan
Chair(s): Tailen Hsing, University of Michigan
2:05 PM	Testing in High-Dimensional Spiked Models Iain Johnstone, Stanford University; Alexei Onatski, Cambridge University
2:30 PM	Convergence Rates of Least Squares Regression Estimators with Heavy-Tailed Errors Qiyang Han, Rutgers University; Jon A. Wellner, University of Washington
2:55 PM	The Two-to-Infinity Norm and Singular Subspace Geometry Presentation Carey E Priebe, Johns Hopkins University; Minh Tang, Johns Hopkins University; Joshua Cape, Johns Hopkins University
3:20 PM	Efficient Nonparametric Bayesian Inference for X-Ray Transforms Richard Nickl, University of Cambridge
3:45 PM	Floor Discussion

549 * !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-107
Optimal Designs for Modeling Asymmetries in Big Data — Invited Papers
WNAR, IMS, International Chinese Statistical Association
Organizer(s): Milan Stehlik, Johannes Kepler University and University of Valparaiso
Chair(s): Ying Lu, Stanford University
2:05 PM	Optimal Experimental Designs for Skewed Data via Cuckoo Algorithm Presentation Guanghao Qi, Johns Hopkins University; Weng Kee Wong, UCLA
2:30 PM	Subdata Selection Methods Presentation John Stufken, Arizona State University
2:55 PM	Adjusting for Bias Induced by Informative Adaptive Designs Presentation Nancy Flournoy, University of Missouri; Assaf P Oron, Institute for Disease Modeling
3:20 PM	Discussant: Milan Stehlik, Johannes Kepler University and University of Valparaiso
3:40 PM	Floor Discussion

554 * !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-503
Interdisciplinary Research and Leadership: How to Make an Impact in the Data Science Age — Invited Panel
IMS, Section on Statistical Learning and Data Science, Royal Statistical Society
Organizer(s): Bin Yu, UC Berkeley
Chair(s): Bin Yu, UC Berkeley
2:05 PM	Interdisciplinary Research and Leadership: How to Make an Impact in the Data Science Age
Panelists:	Alicia Carriquiry, Iowa State University Christopher Genovese, Statistics, CMU Hongyu Zhao, Yale Jasjeet Sekhon, UC Berkeley Simon Tavare, Inst of Cancer Dynamics and Statistics, Columbia University Tamara Tamara Greasby, Data Science at The Trade Desk
3:45 PM	Floor Discussion

559 !	Wed, 7/31/2019, 2:00 PM - 3:50 PM	CC-301
Randomized Algorithms for Optimization Problems in Statistics — Topic Contributed Papers
Section on Statistical Learning and Data Science, IMS, Section on Statistical Computing
Organizer(s): Miles Lopes, UC Davis
Chair(s): Miles Lopes, UC Davis
2:05 PM	Statistical Properties of Stochastic Gradient Descent Presentation Panagiotis Toulis, University of Chicago Booth School of Business; Jerry Chee, University of Chicago
2:25 PM	Randomized Sparse PCA Using the Variable Projection Method N. Benjamin Erichson, Univ of California - Berkeley
2:45 PM	Randomized Linear Algebra and Its Applications in Second-Order Optimization and Deep Learning Presentation Zhewei Yao, UC Berkeley
3:05 PM	Understanding the Acceleration Phenomenon via High-Resolution Differential Equations Weijie Su, University of Pennsylvania
3:25 PM	Random Projections for Faster Non-Convex Optimization Mert Pilanci, Stanford University

584 !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-505
Empirical Processes: Theory and Applications — Invited Papers
IMS, Section on Nonparametric Statistics
Organizer(s): Jon A. Wellner, University of Washington
Chair(s): Jon A. Wellner, University of Washington
8:35 AM	On Nonhomogeneous Random Matrices Ramon van Handel, Princeton University
9:05 AM	Jackknife Multiplier Bootstrap: Finite Sample Approximations to the U-Process Supremum with Applications Kengo Kato, Cornell University; Xiaohui Chen, University of Illinois at Urbana-Champaign
9:35 AM	Limit Distribution Theory for Multiple Isotonic Regression Presentation Qiyang Han, Rutgers University; Cun-Hui Zhang, Rutgers University
10:05 AM	Floor Discussion

587 * !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-506
Post-Selection Inference — Invited Papers
IMS
Organizer(s): Robert Tibshirani, Stanford University
Chair(s): Robert Tibshirani, Stanford University
8:35 AM	Selective Inference, Epistemology and Higher-Order Asymptotics Todd Kuffner, Washington University
9:05 AM	Inference After Black Box Selection Presentation 1 Presentation 2 Jelena Markovic, Stanford University
9:35 AM	Be Careful What You Ask For: How to Ask Statistically "Cheap" (But Useful) Questions for Your Data Keli Liu, Stanford University
10:05 AM	Floor Discussion

598 * !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-113
Statistical Learning with Unconventional Missing Data — Topic Contributed Papers
International Chinese Statistical Association, Section on Statistical Learning and Data Science, IMS
Organizer(s): Gen Li, Columbia University
Chair(s): Jiayi Ji, Icahn School of Medicine at Mount Sinai
8:35 AM	Generalized Integrative Principal Component Analysis for Multi-Type Data with Block-Wise Missing Structure Presentation Gen Li, Columbia University; Eric Lock, University of Minnesota; Huichen Zhu, Columbia University
8:55 AM	How Not to Estimate the Nonignorable Missingness Mechanism Jiwei Zhao, State University of New York At Buffalo
9:15 AM	Using Multivariate Mixed-Effects Selection Models for Analyzing Batch-Processed Proteomics Data with Non-Ignorable Missingness Presentation Lin Chen, University of Chicago; Jiebiao Wang, Carnegie Mellon University; Pei Wang, Icahn School of Medicine at Mount Sinai; Donald Hedeker, University of Chicago
9:35 AM	Floor Discussion

599 !	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-507
Resampling Methods for High-Dimensional Inference — Topic Contributed Papers
IMS, Section on Nonparametric Statistics, International Indian Statistical Association
Organizer(s): Miles Lopes, UC Davis
Chair(s): Guillaume Basse, UC Berkeley
8:35 AM	Higher Order Asymptotic Properties of the Bootstrap in Post Model Selection Inference in High Dimensions Soumendra N Lahiri, North Carolina State University
8:55 AM	One-Way Functional ANOVA via Basis Expansion and Bootstrapping Presentation Zhenhua Lin, University of California, Davis; Miles Lopes, UC Davis; Hans Mueller, UC Davis
9:15 AM	New Non-Asymptotic Results About Accuracy of Bootstrapping Procedures in Multivariate Setting Mayya Zhilova, Georgia Institute of Technology
9:35 AM	Finite Sample Unbiasedness in High Dimensions via the Iterative Bootstrap Stephane Guerrier, University of Geneva
9:55 AM	Floor Discussion

601 *	Thu, 8/1/2019, 8:30 AM - 10:20 AM	CC-705
Recent Advances in Variable Selection for Linear and Nonlinear Models — Topic Contributed Papers
Biometrics Section, Section on Statistical Learning and Data Science, IMS
Organizer(s): Marinela Capanu, Memorial Sloan Kettering Cancer Center
Chair(s): Colin Begg, Memorial Sloan Kettering Cancer Center
8:35 AM	Optimized Variable Selection via Repeated Data Splitting Presentation Marinela Capanu, Memorial Sloan Kettering Cancer Center; Colin Begg, Memorial Sloan Kettering Cancer Center; Mithat Gonen, Memorial Sloan Kettering Cancer Center
8:55 AM	Thresholding Least-Squares for High-Dimensional Regression Models Presentation Mihai Giurcanu
9:15 AM	Metropolized Knockoff Sampling Presentation Stephen Bates, Stanford; Emmanuel Candes, Stanford University; Lucas Janson, Harvard University; Wenshuo Wang, Harvard University
9:35 AM	Nonuniformity of P-Values Can Occur Early in Diverging Dimensions Presentation Emre Demirkaya, University of Southern California
9:55 AM	Model Selection Bias Invalidates Goodness of Fit Tests Presentation Joshua Loftus, New York University
10:15 AM	Floor Discussion

625 !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-103
Modern Non-Parametrics — Invited Papers
IMS
Organizer(s): Veronika Rockova, University of Chicago
Chair(s): Edward George, University of Pennsylvania
10:35 AM	Multi-Scale Analysis of BART Priors Veronika Rockova, University of Chicago; Ismael Castillo, Sorbonne University
11:00 AM	Coverage of Bayesian Credible Sets for Monotone Regression Presentation Subhashis Ghoshal, North Carolina State University; Moumita Chakraborty, North Carolina State University
11:25 AM	Statistical Risk Bounds for Deep Neural Networks Presentation Johannes Schmidt-Hieber, Leiden University
11:50 AM	Just Interpolate: Kernel 'Ridgeless' Regression Can Generalize Tengyuan Liang, University of Chicago Booth School of Business
12:15 PM	Floor Discussion

626	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-104
Recent Advances in High-Dimensional Statistical Inference — Invited Papers
IMS
Organizer(s): Jinyuan Chang, Southwestern University of Finance and Economics
Chair(s): Wen Zhou, Colorado State University
10:35 AM	Subvector Inference in PI Models with Many Moment Inequalities Alexandre Belloni, Duke University; Federico Bugni, Duke University; Victor Chernozhukov, MIT
11:00 AM	High-Dimensional Statistical Inferences with Over-Identification Presentation Jinyuan Chang, Southwestern University of Finance and Economics; Song Xi Chen, Peking University; Cheng Yong Tang, Temple University; Tong Tong Wu, University of Rochester
11:25 AM	Theoretical Support of Machine Learning Debugging Po-Ling Loh, UW-Madison
11:50 AM	Robust Statistics Meets Nonconvex Optimization Presentation Wenxin Zhou, University of California, San Diego; Qiang Sun, University of Toronto
12:15 PM	Floor Discussion

633	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-102
Foundations of Data Science: Privacy-Preserving Inference — Invited Papers
Business and Economic Statistics Section, Royal Statistical Society, IMS, Section on Statistical Learning and Data Science
Organizer(s): Sofia C Olhede, University College London
Chair(s): Guy Nason, University of Bristol
11:00 AM	Privacy-Preserving Technologies Meet Machine Learning Presentation Jeannette Wing, Columbia University, Data Science Institute
11:25 AM	Privacy-Preserving Prediction Presentation 1 Presentation 2 Presentation 3 Cynthia Dwork, Harvard University; Vitaly Feldman, Google
11:50 AM	Discussant: Patrick J Wolfe, Purdue University
12:15 PM	Floor Discussion

636 * !	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-105
Graphical Models: From Foundations to Applications — Invited Papers
IMS
Organizer(s): Caroline Uhler, Massachusetts Institute of Technology
Chair(s): Dominik Rothenhäusler, UC Berkeley
10:35 AM	Total Positivity and Graphical Models Presentation Piotr Zwiernik, Universitat Pompeu Fabra; Caroline Uhler, Massachusetts Institute of Technology
11:00 AM	On the Decomposition of Pairwise Association Measures Along the Paths of an Undirected Concentration Graph Model. Alberto Roverato, University of Padua; Robert Castelo , Universitat Pompeu Fabra
11:25 AM	Algebraic Geometry of Gaussian Graphical Models Presentation Seth Sullivant, North Carolina State University
11:50 AM	Minimax Prediction in Tree Ising Models Guy Bresler, Massachusetts Institute of Technology (MIT)
12:15 PM	Floor Discussion

657	Thu, 8/1/2019, 10:30 AM - 12:20 PM	CC-101
Bayesian and Empirical Bayes — Contributed Papers
IMS
Chair(s): Satyajit Ghosh, Rutgers University
10:35 AM	Hierarchical Bayesian Kernel Model with Applications to Prediction with Small Data Jin-Zhu Yu; Hiba Baroud, Vanderbilt University
10:50 AM	A General Framework for Empirical Bayes Estimation in the Discrete Linear Exponential Family Trambak Banerjee, University of Southern California; Qiang Liu, University of Texas at Austin; Gourab Mukherjee, University of Southern California; Wenguang Sun, University of Southern California
11:05 AM	Protecting Replicability in the Presence of Auxiliary Covariates Pallavi Basu, Indian School of Business; Hema Kollipara, Michigan State University (and Indian School of Business)
11:20 AM	Posterior Inference Under Adaptive Penalization for Quantile Regression Yuanzhi Li, University of Michigan; Xuming He, University of Michigan
11:35 AM	Information Content of High-Order Associations of the Human Gut Microbiota Network Presentation Weston Viles, University of Southern Maine; Juliette C. Madan, The Geisel School of Medicine at Dartmouth; Hongzhe Li, University of Pennsylvania; Jason H Moore, University of Pennsylvania; Margaret R. Karagas, The Geisel School of Medicine at Dartmouth; Anne G. Hoen, The Geisel School of Medicine at Dartmouth
11:50 AM	Hierarchical Bayesian Link Model for Stochastic Frontier Production Function Model Presentation Seongho Song, University of Cincinnati; Younshik Chung, Pusan National University; David T. Yi, Xavier University
12:05 PM	Floor Discussion

JSM 2019 Online Program

Activity Details

American Statistical Association