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CC = Vancouver Convention Centre   F = Fairmont Waterfront Vancouver
* = applied session       ! = JSM meeting theme

Activity Details


11 * !
Sun, 7/29/2018, 2:00 PM - 3:50 PM CC-West 206/207
Daunting Challenges and Innovative Solutions for Big Data Analysis — Invited Papers
Section on Statistical Computing, Caucus for Women in Statistics, Section on Statistical Learning and Data Science, SSC, Social Statistics Section
Organizer(s): Nusrat Jahan, James Madison University
Chair(s): Nusrat Jahan, James Madison University
2:05 PM Spatially Informed Variable Selection Priors and Application to Neuroimaging Data
Presentation
Marina Vannucci, Rice University
2:30 PM Analysis and Visualization for Large-Scale Scientific Simulations
Joanne R. Wendelberger, Los Alamos National Laboratory; Divya Banesh, Los Alamos National Laboratory; James Ahrens, Los Alamos National Laboratory
2:55 PM Nonparametric Empirical Bayes Methods for High Dimension Problems 
Presentation
Linda Zhao, University of Pennsylvania; Junhui Cai, University of Pennsylvania
3:20 PM Data-Driven Regularization and Priors in GWAS and Mediation Analysis
Presentation
Sunduz Keles, University of Wisconsin, Madison
3:45 PM Floor Discussion
 
 

22 * !
Sun, 7/29/2018, 2:00 PM - 3:50 PM CC-West 210
The World of Data Analysis Professionals — Topic Contributed Papers
Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Business Analytics/Statistics Education Interest Group
Organizer(s): Nancy Wang, Celerion
Chair(s): Nancy Wang, Celerion
2:05 PM Bridge the Gap Between Statistician and Data Analysis Professionals
Presentation
Ming Li, Amazon
2:25 PM Boost Your Analytical Power by Utilizing Text Data
Jin Su, Johnson & Johnson Vision Care; Danielle Boree, Johnson & Johnson Vision
2:45 PM Statistics at Consumer Reports
Presentation
Michael Saccucci, Consumer Reports
3:05 PM Developing a Data Science Program; Challenges and Outcomes
Presentation
Mahbubul Majumder, University of Nebraska at Omaha
3:25 PM Discussant: Greg Valin, Amgen, Inc
3:45 PM Floor Discussion
 
 

23 * !
Sun, 7/29/2018, 2:00 PM - 3:50 PM CC-West 205
Recents Advances in Statistical Learning and Network Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science, SSC
Organizer(s): Sijian Wang, Rutgers University
Chair(s): Sijian Wang, Rutgers University
2:05 PM High-dimensional Cost-constrained Regression via Non-convex Optimization
Presentation
Yufeng Liu, University of North Carolina at Chapel Hill
2:25 PM Generalized Bias and Variance for Convex Regularized Estimators
Presentation
Pierre Bellec, Rutgers University
2:45 PM High-Dimensional Gaussian Graphical Model for Network-Linked Data
Presentation
Ji Zhu, University of Michigan; Boang Liu, University of Michigan; Tianxi Li, University of Michigan; Cheng Qian, University of Michigan; Elizaveta Levina, University of Michigan
3:05 PM Toward a Sampling Theory for Statistical Network Analysis
Harry Crane, Rutgers
3:25 PM Network Regression and Inference
Presentation
Peng Wang, University of Cincinnati; Xiaotong Shen, University of Minnesota
3:45 PM Floor Discussion
 
 

28
Sun, 7/29/2018, 2:00 PM - 3:50 PM CC-West 208
SPEED: A Mixture of Topics in Health, Computing, and Imaging — Contributed Speed
Mental Health Statistics Section, Section on Statistical Computing, Section on Statistics in Imaging, Section on Statistical Learning and Data Science, SSC, Section on Physical and Engineering Sciences, Section for Statistical Programmers and Analysts
Chair(s): Lu Chen, Worcester Polytechnic Institute
Poster Presentations for this session.
2:05 PM Remote Perconditioning Enhances Neuro Protection and Collateral Blood Flow During Ischemia in Distal Cerebral Ischemic Rat Model (MCAo) Through AMPK-ENOS Pathways
Presentation
Abdul Salam, Hamad Medical Corporation; Aijaz Parray, Hamad Medical Corporation; yonglie Ma, University of Alberta ; Naveed Akhter, Hamamd Medical Corporation; Sajitha VP, Hamad Medical Corporation; Ruth Priyanka, Hamamd Medical Corporation; Ian Winship, University of Alberta; Nosheen Shahid, Hamamd Medical Corporation; Ashfaq Shuaib, University of Alberta
2:10 PM Polynomial Based Approximate Probability Distributions
Chris Elrod, Baylor University; James Stamey, Baylor University
2:15 PM Measurement Reliability in Mental Health Research: Critical Implications for Research Design and Analysis
Alessandro De Nadai, Texas State University; Marieke Visser, Texas State University
2:30 PM Tailoring PCA for Detecting Sparse Changes in Multi-Stream Data
Presentation
Martin Tveten, University of Oslo; Ingrid Kristine Glad, University of Oslo
2:35 PM Ranked Sparsity Methods for Transparent Model Selection
Presentation
Ryan Andrew Peterson, University of Iowa; Joseph Cavanaugh, University of Iowa
2:40 PM Image-On-Image Regression: a Spatial Bayesian Latent Factor Model for Predicting Task-Evoked Brain Activity Using Task-Free MRI
Presentation
Cui Guo, University of Michigan
2:45 PM Fusion of the Semiparametric Models and Network Measures in the Study of Brain Dynamic Functional Connectivity
Maria Kudela, Takeda Pharmaceuticals; Jaroslaw Harezlak, Indiana University Bloomington; Mario Dzemidzic, Indiana University School of Medicine; Brandon Oberlin, Indiana University School of Medicine; David A Kareken, Indiana University School of Medicine; Joaquin Goni, Purdue University
2:50 PM Fast Generalised Linear Models in a Database
Presentation
Thomas Lumley, University of Auckland
3:00 PM A Deep Learning Approach to the Estimation of Bias and Variance in HARDI
Presentation
Allison Hainline, Vanderbilt University; Hakmook Kang, Vanderbilt University Medical Center; Bennett Landman, Vanderbilt University
3:05 PM Creating Counting Process Intervals with Ease
Presentation
Cynthia Crowson, Mayo Clinic; Terry M Therneau, Mayo Clinic; Elizabeth J Atkinson, Mayo Clinic
3:10 PM Multi-Scale Vecchia Approximation of Gaussian Processes
Presentation
Jingjie Zhang, Texas A&M University; Matthias Katzfuss, Texas A&M University
 
 

39
Sun, 7/29/2018, 2:00 PM - 3:50 PM CC-West 204
Topics in Clustering — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Shanghong Xie, Columbia University
2:05 PM Sparse Convex Clustering
Presentation
Binhuan Wang, New York University School of Medicine; Yilong Zhang, Merck Research Laboratories; Will Wei Sun, University of Miami School of Business Administration; Yixin Fang, New Jersey Institute of Technology
2:20 PM Finite Mixture-Of-Gamma Distributions: Estimation, Inference, and Model-Based Clustering
Presentation
Derek S. Young, University of Kentucky; Xi Chen, University of Kentucky; Dilrukshi Hewage, University of Kentucky; Ricardo N. Poyanco, FONDAP Center for Genome Regulation
2:35 PM Mixture Model Modal Clustering
Presentation
Jose Chacon, Universidad De Extremadura
2:50 PM Exploring Clustering Applications in Outlier Detection for Administrative Data Sources
Presentation
Elizabeth Ayres, Statistics Canada
3:05 PM Hierarchical Significance Testing for Gaussian Mixture Clustering
Presentation
Purvasha Chakravarti, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University; Sivaraman Balakrishnan, Carnegie Mellon University
3:20 PM Regularized Aggregation of Statistical Parametric Maps
Presentation
Cheolwoo Park, University of Georgia; Li-Yu Wang, University of Georgia; Jongik Chung, University of Georgia; Hosik Choi, University of Georiga; Amanda Rodrigue, University of Georgia; Jordan Pierce, University of Georgia; Brett Clementz, University of Georgia; Jennifer McDowell, University of Georgia
3:35 PM Floor Discussion
 
 

43 * !
Sun, 7/29/2018, 4:00 PM - 5:50 PM CC-West 304/305
Discovering Homology in Multi-View Data: New Statistical Methods for Data Integration — Invited Papers
ENAR, Section on Statistical Learning and Data Science, Biometrics Section
Organizer(s): Irina Gaynanova, Texas A&M Univeristy
Chair(s): Irina Gaynanova, Texas A&M Univeristy
4:05 PM Clustering Multiple-View Data: Are Two Clusterings Independent?
Presentation
Lucy Gao, University of Washington; Jacob Bien, University of Southern California; Daniela Witten, University of Washington
4:30 PM Angle Based Joint and Individual Variation Explained
Presentation
J. S. (Steve) Marron, University of North Carolina; Jan Hannig, University of North Carolina; Meilei Jiang, University of North Carolina; Qing Feng, Uber
4:55 PM Integrated Reduced-Rank Models with Multiple Sets of Predictors
Presentation
Gen Li, Columbia University; Kun Chen, University of Connecticut
5:20 PM Joint Modeling of Multi-System Wearable Data
Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health; Junrui Di, Johns Hopkins Bloomberg School of Public Health
5:45 PM Floor Discussion
 
 

46 * !
Sun, 7/29/2018, 4:00 PM - 5:50 PM CC-West 121
Recent Advances in Cluster Analysis and Cluster Validation — Invited Papers
Section on Statistical Learning and Data Science, SSC
Organizer(s): Daniel Fernandez, Victoria University of Wellington
Chair(s): Alexander Foss, Sandia National Laboratories
4:05 PM A Mixture of Matrix Variate Bilinear Factor Analyzers
Paul McNicholas, McMaster University
4:30 PM Clustering with Topic Models
Presentation
David Banks, Duke University
4:55 PM Think Before You Cluster: Testing for Clusterability
Presentation
Naomi Brownstein, Florida State University; Margareta Ackerman, Santa Clara University; Andreas Adolfsson, Santa Clara University; Zachariah Neville, Florida State University
5:20 PM Cluster Validation by Measurement of Clustering Characteristics Relevant to the User
Presentation
Christian Hennig, University College London
5:45 PM Floor Discussion
 
 

47 * !
Sun, 7/29/2018, 4:00 PM - 5:50 PM CC-East 10
Statistical Analysis of Linked Data — Invited Papers
Survey Research Methods Section, Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science, Caucus for Women in Statistics, Social Statistics Section
Organizer(s): Ying Han, University of Maryland, College Park; Partha Lahiri, University of Maryland, College Park
Chair(s): Daniel Bonnery, University of Maryland
4:05 PM Outlier Robust Inference Using Probabilistically Linked Data
Presentation
Nicola Salvati, University of Pisa; Suojin Wang, Texas A&M University; Enrico Fabrizi, Catholic University of Sacro Cuore; Raymond Chambers, University of Wollongong
4:30 PM Entity Resolution with Societal Impacts in Statistical Machine Learning
Presentation
Rebecca C. Steorts, Duke University
4:55 PM A Bayesian Approach for Deduplication, Record Linkage, and Inference with Linked Data
Presentation
brunero liseo, Sapienza Università di Roma; Andrea Tancredi, Sapienza Università di Roma; Rebecca C. Steorts, Duke University
5:20 PM Discussant: Mauricio Sadinle, University of Washington
5:45 PM Floor Discussion
 
 

49 * !
Sun, 7/29/2018, 4:00 PM - 5:50 PM CC-West 213
Skills to Leverage and Gaps to Fill to Thrive in Data Science — Invited Papers
Section on Statistical Consulting, Committee on Applied Statisticians, Section on Statistical Learning and Data Science, SSC, Quality and Productivity Section
Organizer(s): Eric Vance, LISA-University of Colorado Boulder
Chair(s): James L Rosenberger, NISS (National Institute of Statistical Sciences) and Penn State
4:05 PM Communication and Collaboration Skills for the Era of Data Science
Presentation
Eric Vance, LISA-University of Colorado Boulder
4:30 PM From Academia to Industry: Statistical Skills That Translate
Olivia Lau, Google
4:55 PM What Statisticians Need to Know to Work in Tech
Michael Brundage, Google, Inc.
5:45 PM Floor Discussion
 
 

50 *
Sun, 7/29/2018, 4:00 PM - 5:50 PM CC-West 122
Which Sessions Should This Go To? Text Analytics to the Rescue of Conference Committees — Invited Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, Stats. Partnerships Among Academe Indust. & Govt. Committee
Organizer(s): Stas Kolenikov, Abt Associates
Chair(s): Jeffrey Gonzalez, Bureau of Labor Statistics
4:05 PM Text Mining Using Discrete Optimization
Presentation
Jason Pan, Pfizer Inc; Kelly H Zou, Pfizer Inc; Ching-Ray Yu, Pfizer Inc
4:30 PM Creating a Taxonomy of Statistical Methods Using Text Analysis
Presentation
Wendy L Martinez, Bureau of Labor Statistics
4:55 PM Identifying and Utilizing Research Topics in Conference Abstracts
Stas Kolenikov, Abt Associates; Alison Thaung, Abt Associates
5:20 PM Discussant: Julia D Silge, Stack Overflow
5:45 PM Floor Discussion
 
 

79
Sun, 7/29/2018, 4:00 PM - 5:50 PM CC-West 119
Statistical Analysis for Networks — Contributed Papers
Section on Statistical Learning and Data Science, SSC
Chair(s): Cheolwoo Park, University of Georgia
4:05 PM Estimating Heterogeneous Biomarker Networks and Their Effects on Disease Outcome
Shanghong Xie, Columbia University; Xiang Li, Statistics and Decision Sciences, Janssen Research & Development, LLC; Donglin Zeng, UNC Chapel Hill; Yuanjia Wang, Columbia University
4:35 PM New Methods for Incorporating Network Cyclic Structures to Improve Community Detection
Presentation
Behnaz Moradijamei, Kansas State University; Michael Higgins, KANSAS STATE UNIVERSITY; Heman Shakeri, Kansas State University
5:20 PM Extendability for Exchangeable Network Models
Jiaqi Yin, University of Washington; Thomas Richardson, University of Washington
5:35 PM Floor Discussion
 
 

84
Sun, 7/29/2018, 4:00 PM - 4:45 PM CC-West Hall B
SPEED: A Mixture of Topics in Health, Computing, and Imaging — Contributed Poster Presentations
Mental Health Statistics Section, Section on Statistical Computing, Section on Statistics in Imaging, Section on Statistical Learning and Data Science, SSC, Section on Physical and Engineering Sciences, Section for Statistical Programmers and Analysts
Chair(s): Paul McNicholas, McMaster University
Oral Presentations for this session.
21: Remote Perconditioning Enhances Neuro Protection and Collateral Blood Flow During Ischemia in Distal Cerebral Ischemic Rat Model (MCAo) Through AMPK-ENOS Pathways
Abdul Salam, Hamad Medical Corporation; Aijaz Parray, Hamad Medical Corporation; yonglie Ma, University of Alberta ; Naveed Akhter, Hamamd Medical Corporation; Sajitha VP, Hamad Medical Corporation; Ruth Priyanka, Hamamd Medical Corporation; Ian Winship, University of Alberta; Nosheen Shahid, Hamamd Medical Corporation; Ashfaq Shuaib, University of Alberta
22: Polynomial Based Approximate Probability Distributions
Chris Elrod, Baylor University; James Stamey, Baylor University
23: Measurement Reliability in Mental Health Research: Critical Implications for Research Design and Analysis
Alessandro De Nadai, Texas State University; Marieke Visser, Texas State University
27: Tailoring PCA for Detecting Sparse Changes in Multi-Stream Data
Martin Tveten, University of Oslo; Ingrid Kristine Glad, University of Oslo
28: Ranked Sparsity Methods for Transparent Model Selection
Ryan Andrew Peterson, University of Iowa; Joseph Cavanaugh, University of Iowa
29: Image-On-Image Regression: a Spatial Bayesian Latent Factor Model for Predicting Task-Evoked Brain Activity Using Task-Free MRI
Cui Guo, University of Michigan
30: Fusion of the Semiparametric Models and Network Measures in the Study of Brain Dynamic Functional Connectivity
Maria Kudela, Takeda Pharmaceuticals; Jaroslaw Harezlak, Indiana University Bloomington; Mario Dzemidzic, Indiana University School of Medicine; Brandon Oberlin, Indiana University School of Medicine; David A Kareken, Indiana University School of Medicine; Joaquin Goni, Purdue University
31: Fast Generalised Linear Models in a Database
Thomas Lumley, University of Auckland
32: A Deep Learning Approach to the Estimation of Bias and Variance in HARDI
Allison Hainline, Vanderbilt University; Hakmook Kang, Vanderbilt University Medical Center; Bennett Landman, Vanderbilt University
33: Creating Counting Process Intervals with Ease
Cynthia Crowson, Mayo Clinic; Terry M Therneau, Mayo Clinic; Elizabeth J Atkinson, Mayo Clinic
34: Multi-Scale Vecchia Approximation of Gaussian Processes
Jingjie Zhang, Texas A&M University; Matthias Katzfuss, Texas A&M University
Oral Presentations for this session.
 
 

87
Sun, 7/29/2018, 8:30 PM - 10:30 PM CC-West Hall B
Invited ePoster Session: a Statistical Smörgåsbord — Invited Poster Presentations
SSC, Section on Bayesian Statistical Science, Section on Statistics in Epidemiology, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Biometrics Section, Section on Statistics and the Environment, Section for Statistical Programmers and Analysts, Section on Statistics in Imaging, WNAR, Social Statistics Section, Astrostatistics Special Interest Group, Biopharmaceutical Section, ENAR, Section on Risk Analysis, Section on Statistical Consulting
Chair(s): Paul McNicholas, McMaster University
1: The LISA 2020 Program to Build Statistics Capacity in Developing Countries
Eric Vance, LISA-University of Colorado Boulder
2: Conditions for the Uniqueness, Finiteness, and Possible Location of the Maximum Likelihood Estimate with a Log Binomial Model
Gurbakhshash Singh, University of Calgary; Gordon Hilton Fick, University of Calgary
3: Two Mixture-Based Clustering Approaches: Modeling an Automobile Insurance Portfolio
Tatjana Miljkovic, Miami University; Daniel Fernandez, Victoria University of Wellington
4: An Expectation Conditional Maximization Approach for Gaussian Graphical Models
Zehang Li, University of Washington; Tyler McCormick, University of Washington
5: A Bayesian Model for Multivariate Micro-Level Insurance Claims
Marie-Pier Côté, Universite Laval; Christian Genest, McGill University; David A Stephens, McGill University
6: Deep Learning for Statistical Inference in Infectious Disease Systems
Rob Deardon, University of Calgary; Carolyn Augusta, University of Guelph; Graham Taylor, University of Guelph
7: Flexible Accelerated Failure Time Model in Survival Analysis
Menglan Pang, McGill University; Michal Abrahamowicz, McGill University; Robert W Platt, McGill University
8: Spatio-Temporal Analysis of Children and Adolescents' Emergency Department Use for Mental Health Reasons in Alberta, Canada
Michelle Thiessen, Simon Fraser University; Joan Hu, Simon Fraser University; Rhonda J. Rosychuk, University of Alberta
9: Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Taylor Gene Pospisil, Carnegie Mellon University
10: Zero Counts in Single Cell RNA-Seq Data
Hao Wu, Emory University; Zhijin Wu, Brown University
11: Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Xinkun Nie, Stanford University; Stefan Wager, Stanford University
12: Estimation of Fire Duration Distribution with Missing Start Time
Yi Xiong, Simon Fraser University; John Braun, University of British Columbia ; Joan Hu, Simon Fraser University
13: Bayesian Nonparametric Hierarchical Models for Lightcurve Classification and Observation Decisions
David Edward Jones, Duke University and SAMSI; Sujit Ghosh, North Carolina State Univ.; Ana-Maria Staicu, NC State University; Ashish Mahabal, Caltech
14: Approximate Bayesian Computation for the Stellar Initial Mass Function
Jessi Cisewski-Kehe, Yale University; Chad Schafer, Carnegie Mellon University; Grant Weller, Savvysherpa; David Hogg, New York University
15: A Novel Bayesian Framework to Probe Closed Box Nature of Galaxy Clusters
Arya Farahi, University of Michigan - Ann Arbor
16: Statistical Approaches to Decreasing the Discrepancy of Non-Detects in QPCR Data
Valeriia Sherina, University of Rochester Medical Center; Love Tanzy, University of Rochester Medical Center; Matthew N. McCall, University of Rochester Medical Center
17: Generalized Statistical Inference for Astrophysical Discoveries
Sara Algeri, Imperial College London; David A van Dyk, Imperial College London; Jan Conrad, Oskar Klein Centre for Cosmoparticle Physics
18: Nonparametric Causal Effects Based on Incremental Propensity Score Interventions
Edward Kennedy, Carnegie Mellon University; Matteo Bonvini, Carnegie Mellon University
19: Addressing Overfitting in Mixtures of Factor Analyzers
Jeffrey L Andrews, University of British Columbia Okanagan
20: Spatiotemporal Analysis of Environmental Health Risk
Renjun Ma, University of New Brunswick; Edward Hughes, Edward Hughes Consulting
21: Probabilistic Partial Least Squares Regression Applied to Longitudinal and Cross-Sectional Compositional Data
Peter A Tait, McMaster Univeristy; Paul McNicholas, McMaster University
22: Detection of Trend Onset in Environmental Time Series
Ying Zhang, Acadia University
23: The Analysis of Face Perception MEG and EEG Data Using a Potts-Mixture Spatiotemporal Joint Model
Yin Song, University of Victoria; Farouk Nathoo; Arif Babul, University of Victoria
24: Infere
Steven Cumming, Université Laval
25: Functional Partial Linear Quantile Regression Based on Reproducing Kernel Hilbert Space
Peng Liu, University of Alberta; Linglong KONG, University of Alberta; Bei JIANG, University of Alberta; Nan Zhang, Fudan University; Jianhua Z. Huang, Texas A&M University
26: Gaussian Process Regression with Large Data Sets: Has the Problem Been Solved?
Sonja Surjanovic, University of British Columbia; William Welch, University of British Columbia
27: Sparse Estimation for Functional Semiparametric Additive Model
Peijun Sang, Simon Fraser University; Richard Lockhart, Simon Fraser University; Jiguo Cao, Simon Fraser University
28: Analysis of Paired Binary Data Subject to Misclassification Using a Random Effect Model
Hua Shen, University of Calgary ; Richard John Cook, University of Waterloo
29: A Grouped Weighted Quantile Regression Approach to Modeling Environmental Chemical Mixtures and Childhood Leukemia Risk
David C. Wheeler, Virginia Commonwealth University
30: Efficient Robust Doubly Adaptive Regularized Regression with Application to fMRI Data
Wei Tu, University of Alberta
31: A Model-Based Clustering to Identify Disease-Associated SNPs
Li Xing, University of Victoria; Xuekui Zhang, University of Victoria; Yan Xu, University of Victoria; Weiliang Qiu, Brigham and Women's Hosptial/Harvard Medical School
32: The Consequences of Requiring 'Greater Statistical Stringency' for Scientific Publication
Harlan Campbell, University of British Columbia; Paul Gustafson, University of British Columbia
33: Mixtures of Contaminated Shifted Asymmetric Laplace Factor Analyzers
Brian C Franczak, MacEwan University
34: Uncertainty Quantification of Stochastic Computer Model for Binary Black Hole Formation
Luyao Lin, Simon Fraser University; Jim Barrett, University of Birmingham; Derek Bingham, Simon Fraser University; Ilya Mandel, University of Birmingham
35: Network Meta-Analysis of Disconnected Networks: How Dangerous Are Random Baseline Treatment Effects?
Audrey Béliveau, University of Waterloo; Sarah Goring, SMG Outcomes Research; Robert W Platt, McGill University; Paul Gustafson, University of British Columbia
36: Nonparametric Measures of Local Causality and Tests of Local Non-Causality in Time Series
Felix Camirand Lemyre, School of mathematics and statistics, University of Melbourne; Taoufik Bouezmarni, Université de Sherbrooke; Jean-François Quessy, Université du Québec à Trois-Rivières
37: Sparse Functional Principal Component Analysis in a New Regression Framework
YUNLONG NIE, Simon Fraser University; Jiguo Cao, Simon Fraser University
38: Inference of Introgressive Hybridization in Anopheles Mosquito Genomes
Jingxue(Grace) Feng, Simon Fraser University; Liangliang Wang, Simon Fraser University; Cedric Chauve, Simon Fraser University
39: Statistical Methods for Addressing Missing Data in HIV/AIDS Surveillance Systems
Sahar Zangeneh, Fred Hutchinson Cancer Research Center; Ying Qing Chen, Fred Hutchinson Cancer Research Center; Deborah Donnell, Fred Hutch
40: Latent Mixtures of Functions to Characterize the Complex Exposure Relationships of Pesticides on Cancer Incidence
Sung Duk Kim, National Cancer Institute; Paul S Albert, National Cancer Institute
 
 

104 !
Mon, 7/30/2018, 8:30 AM - 10:20 AM CC-West 110
Visualization and Reproducibility - Challenges and Best Practices — Invited Papers
Section on Statistical Graphics, Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science, Section on Statistical Computing, SSC
Organizer(s): Wendy L Martinez, Bureau of Labor Statistics
Chair(s): John L. Eltinge, United States Census Bureau
8:35 AM EDA: A Historical Perspective and a Path Forward
Dianne Cook, Monash University
9:05 AM The Extended Reproducibility Phenotype - Interactive Graphics Edition
Gabriel Becker, Genentech Research; Vivek Ramaswamy, Genentech Research; Nolan Nichols, Genentech Research; Altaf Kassam, Genentech Research; Dinakar Kulkarni, Genentech Research
9:35 AM A Unified Approach to Exploration, Authoring, and Communication with Reproducible Visualizations
Nils Gehlenborg, Harvard Medical School
10:05 AM Floor Discussion
 
 

112 *
Mon, 7/30/2018, 8:30 AM - 10:20 AM CC-West 122
Smoothing for Spatially and Temporally Indexed Data — Topic Contributed Papers
Royal Statistical Society, Section on Nonparametric Statistics, ENAR, Section on Statistical Learning and Data Science
Organizer(s): Philip Reiss, University of Haifa
Chair(s): Michael Lavine, University of Massachusetts, Amherst
8:35 AM Some Model-Building Tools for Gaussian Processes, Using an Approximate Form of the Restricted Likelihood
Presentation
Maitreyee Bose, University of Washington; James S. Hodges, University of Minnesota; Sudipto Banerjee, UCLA School of Public Health
8:55 AM Flexible Group Difference Tests for Age-Varying Distributions
Presentation
Philip Reiss, University of Haifa
9:15 AM Lagged Hierarchical Semiparametric Models for Task-Based Dynamic Functional Connectivity (DFC)
Presentation
Jaroslaw Harezlak, Indiana University Bloomington; Zikai Lin, Indiana University; Maria Kudela, Takeda Pharmaceuticals; Brandon Oberlin, Indiana University School of Medicine; Joaquin Goni, Purdue University; David A Kareken, Indiana University School of Medicine; Mario Dzemidzic, Indiana University School of Medicine
9:35 AM Methods for Large Scale Smooth Space Time Modeling
Presentation 1 Presentation 2
Simon Wood
10:15 AM Floor Discussion
 
 

116 *
Mon, 7/30/2018, 8:30 AM - 10:20 AM CC-West 120
Modern Advances in Record Linkage Using Statistical Learning Methods — Topic Contributed Papers
Section on Statistical Learning and Data Science, Survey Research Methods Section
Organizer(s): Andee Kaplan, Duke University
Chair(s): Ben Sherwood, University of Kansas
8:35 AM Counting Casualties in the Syrian Civil War with Bayesian Record Linkage
Andrea Kaplan, Duke University; Rebecca C. Steorts, Duke University
8:55 AM Breaking Computational Chicken-And-Egg Loop in Adaptive Sampling and Estimations Using Locality Sensitive Sampling (LSS)
Anshumali Shrivastava, Rice University
9:15 AM UNIQUE ENTITY ESTIMATION with APPLICATION to the SYRIAN CONFLICT
Beidi Chen, Rice University
9:35 AM Discussant: Patrick Ball, Human Rights Data Analysis Group
9:55 AM Discussant: Michele Peruzzi
10:15 AM Floor Discussion
 
 

119
Mon, 7/30/2018, 8:30 AM - 10:20 AM CC-West 213
SPEED: Government and Health Policy — Contributed Speed
Health Policy Statistics Section, Government Statistics Section, Section on Statistical Learning and Data Science, Section on Teaching of Statistics in the Health Sciences, Section for Statistical Programmers and Analysts
Chair(s): Mojca Bavdaz, University of Ljubljana
Poster Presentations for this session.
8:35 AM DataSifter: Statistical Obfuscation of Electronic Health Record and Other Sensitive Data Sets
Nina Zhou, University of Michigan; Simeone Marino, Statistics Online Computational Resource, University of Michigan; Lu Wang, University of Michigan; Yiwang Zhou, University of Michigan; Ivo Dinov, Statistics Online Computational Resource, University of Michigan
8:40 AM Deep Learning on Small Data - Experiences in Transfer Learning for Healthcare
Presentation
Dennis Murphree
8:45 AM Doing More with Less - Eliminating the Long Survey Forms from the Occupational Employment Statistics Survey
Presentation
Carrie K. Jones, US Bureau of Labor Statistics
9:00 AM Statistically Supporting Health Policy Decision-Making
Presentation
Frank Yoon, IBM Watson Health
9:05 AM Intravenous Fluid Treatments for Ebola Patients: The Risk and the Reward
Presentation
Derrick Yam, Brown University; Tao Liu, Brown University; Adam Levine, Brown University; Adam Aluisio, Brown University; Shiromi Peters, International Medical Corps; Suzanne Averill, International Medical Corps; Stephen Kennedy, Ministry of Health, Liberia; Fodey Sahr, Sierra Leone Ministry of Defence; Jillian Peters, Brown University; Daniel Cho, Brown University
9:10 AM Comparison of Methods for Predicting High-Cost Patients Captured Within the Oncology Care Model (OCM): a Simulation Study
Presentation 1 Presentation 2
Jung-Yi Lin, Icahn School of Medicine at Mount Sinai; Wei Zhang, UALR; Mark Liu, Mount Sinai Health System; Mark Sanderson, Mount Sinai Health System; Luis Isola, Mount Sinai Health System; Madhu Mazumdar, Icahn School of Medicine at Mount Sinai; Liangyuan Hu, Icahn School of Medicine at Mount Sinai
9:15 AM Intervening on the Data to Improve the Performance of Health Plan Payment Methods
Presentation
Savannah Bergquist, Harvard University; Tim Layton, Harvard Medical School; Tom McGuire, Harvard Medical School; Sherri Rose, Harvard Medical School
9:20 AM Developing and Evaluating Methods for Estimating Race/Ethnicity in an Incomplete Dataset Using Address, Surname and Family Race
Presentation
Gabriella Christine Silva, Brown University; Roee Gutman, Brown University
9:30 AM Can Post-Stratification Weighting Eliminate the Need for Additional Weights Adjustments?
Chrishelle Lawrence, U.S. Energy Information Administration
9:35 AM Open Data Sharing and Its Statistical Limitations
Presentation
Pooja Iyer, RTI International; Barbara Do, RTI International
9:40 AM Predictors of Hospitalization During a Medicare Skilled Nursing Facility Stay
Presentation
Fei Han, The Hilltop Institute; Ian Stockwell, The Hilltop Institute
9:45 AM Comparison of Treatment Policies Using Bayesian Nonparametric G-Formula
Presentation
Yizhen Xu, Brown University; Tao Liu, Brown University; Rami Kantor, Brown University; Joseph W Hogan, Brown University School of Public Health
9:50 AM Optimal Matching Approaches in Health Policy Evaluations Under Rolling Enrollment
Presentation
Jonathan Gellar, Mathematica Policy Research; Jiaqi Li, Mathematica Policy Research; Lauren Vollmer, Mathematica Policy Research
9:55 AM Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations
Presentation
Maricela Cruz, University of California, Irvine; Miriam Bender, University of California, Irvine; Daniel L. Gillen, University of California, Irvine; Hernando Ombao, King Abdullah University of Science and Technology
10:00 AM Absence of Evidence Is Not Evidence of Absence: a Better Parallel Trends Test
Presentation
Alyssa Bilinski, Harvard Graduate School of Arts and Sciences; Laura Hatfield, Harvard Medical School
10:05 AM New Applications of Machine Learning to Estimating Large Physician Demand Models
Presentation
Bryan Sayer, Social & Scientific Systems, Inc.; William Encinosa, Agency for Health Care Quality and Research
10:10 AM On Utilizing Published Prevalence Estimates to Perform Difference-In-Difference Tests: Testing the Impact of Recreational Marijuana Laws
Presentation
Christine Mauro, Columbia University; Chen Chen, New York State Psychiatric Institute; Silvia Martins, Columbia University; Magda Cerdá, University of California, Davis; Melanie M. Wall, Columbia University
10:15 AM Community Detection with Dependent Connectivity
Yubai Yuan, University of Illinois at Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
 
 

132
Mon, 7/30/2018, 8:30 AM - 10:20 AM CC-West 119
Statistical Analysis for Networks — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Ray-Bing Chen, National Cheng Kung University, Taiwan
8:35 AM Factor Models for High-Dimensional Dynamic Networks: With Application to International Trade Flow Time Series 1981--2015
Presentation
Elynn CHEN, Rutgers University; Rong Chen, Rutgers University
8:50 AM Segmenting Dynamic Network Data
Presentation
Rex Cheung, San Francisco State University
9:05 AM Inferring Low-Rank Population Structure from Multiple Network Samples
Presentation
Keith Levin, University of Michigan; Asad Lodhia, University of Michigan; Elizaveta Levina, University of Michigan
9:20 AM Modeling Sporadic Event Dynamics with Markov-Modulated Hawkes Processes
Presentation
Jing Wu, Columbia University; Tian Zheng, Columbia University
9:35 AM Fast Scalable Random Graph Change Point Estimation via Random Sampling
Mingyuan Gao, University of Florida; Moulinath Banerjee, University of Michigan; George Michailidis, University of Florida
9:50 AM Designing A/B Tests in a Collaboration Network
Sangho Yoon, Google
10:05 AM Floor Discussion
 
 

Register CE_17C
Mon, 7/30/2018, 8:30 AM - 5:00 PM CC-East 11
Nonparametric Regression and Classification for Modern Data Scientists (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section on Statistical Learning and Data Science
Instructor(s): David Banks, Duke University; Margaret Johnson, SAMSI
This course surveys fundamental concepts in modern data science. It emphasizes nonparametric regression and classification, with sparsity, regularization, and the Curse of Dimensionality being recurring themes. Specific topics include: (1) nonparametric regression, including the backfitting algorithm, with the bootstrap and cross-validation as associated tools, (2) the Lasso, elastic net, and LARS, with the Hoff algorithm for solutions when the penalty function is Lq for 0 < q < 1, (3) the p >> n problem, with a survey of key results from Donoho and Tanner, Candes and Tao, and Wainwright, (4) the median model of Berger and Barbieri, (5) comparison of geometric, algorithmic and probabilistic classification methodology, including nearest-neighbor,support vector machine, and Random Forests techniques, (6) improvement of classification techniques through ensembles and forward stagewise learning, such such as bagging, stacking and boosting, (7) topic modeling, using Latent Dirichlet Allocation. Most ideas will be illustrated through an application to a data set.
8:30 AM Nonparametric Regression and Classification for Modern Data Scientists (ADDED FEE)
David Banks, Duke University; Margaret Johnson, SAMSI
 
 

139 * !
Mon, 7/30/2018, 10:30 AM - 12:20 PM CC-West 109
Competing Effectively: Hosting, Designing, and Participating in Kaggle-Style Competitions — Invited Papers
Section on Statistics in Defense and National Security, Section on Statistical Learning and Data Science, Section on Physical and Engineering Sciences, Quality and Productivity Section
Organizer(s): Kary Myers, Los Alamos National Laboratory
Chair(s): Mike Grosskopf, Simon Fraser University
10:35 AM Effective Data Competition Hosting: Strategic Design and Analysis to Maximize Learning
Presentation
Christine M Anderson-Cook, Los Alamos National Laboratory; Kary Myers, Los Alamos National Laboratory
11:05 AM Bayesian Design of Experiments with Multiple Priors for Kaggle Competition Design
Presentation
Kevin Randal Quinlan, The Pennsylvania State University; Christine M Anderson-Cook, Los Alamos National Laboratory
11:35 AM General Techniques for Successful Data Science Competitions
Presentation
Ian Michael Mouzon, Iowa State University
12:05 PM Floor Discussion
 
 

141 * !
Mon, 7/30/2018, 10:30 AM - 12:20 PM CC-West 306
Recent Advances in High-Dimensional Bayesian Model Selection — Invited Papers
International Indian Statistical Association, Section on Bayesian Statistical Science, Section on Statistical Learning and Data Science
Organizer(s): Naveen Naidu Narisetty, University of Illinois at Urbana Champaign
Chair(s): Naveen Naidu Narisetty, University of Illinois at Urbana Champaign
10:35 AM Fully Bayesian Spectral Methods for Imaging Data
Presentation
Brian Reich, North Carolina State University; Joseph Guinness, NC State University; Simon Vandekar, University of Pennsylvania; Russell T Shinohara, University of Pennsylvania; Ana-Maria Staicu, NC State University
11:00 AM Neuronized Priors for A Unified Sparsity Inference
Presentation
Ismael Castillo, Universite Pierre et Marie Curie - Paris 6; Minsuk Shin, Harvard University
11:25 AM Statistical Properties of Variational Bayes
Presentation
Anirban Bhattacharya, Texas A&M University; Debdeep Pati, Texas A&M University; Yun Yang, Florida State University
12:15 PM Floor Discussion
 
 

147 * !
Mon, 7/30/2018, 10:30 AM - 12:20 PM CC-West 110
High-Dimensional Time Series Analysis and Its Applications — Invited Papers
Section on Statistical Learning and Data Science, Statistical and Applied Mathematical Sciences Institute, Committee on Applied Statisticians, SSC
Organizer(s): Ivor Cribben, University of Alberta
Chair(s): Marina Vannucci, Rice University
10:35 AM Analysis of Rapidly Evolving Multivariate Oscillations
Sofia C Olhede, University College London; Adam Sykulski, Lancaster; Arthur Guillaumin, UCL; Jonathan Lilly, NWRA; Jeffrey Earley, NWRA
11:00 AM A Joint Analysis of Brain Signal, Genetics, and Behavior
Zhaoxia Yu, UCI; Hernando Ombao, King Abdullah University of Science and Technology; Dustin Pluta, University of California, Irvine; Tong Shen, University of California, Irvine
11:25 AM Understanding Cryptocurrency Price Formation from Time Series of Local Blockchain Graph Features
Presentation
Cuneyt Akcora, University of Texas at Dallas; Asim Dey, University of Texas at Dallas; Ceren Abay, University of Texas at Dallas; Yulia Gel, University of Texas at Dallas; Umar Islambekov, University of Texas at Dallas; Murat Kantarcioglu, University of Texas at Dallas
11:50 AM Bayesian Approaches for Estimating Dynamic Functional Network Connectivity in fMRI Data
Presentation
Michele Guindani, University of California, Irvine; Erik B. Erhardt, University of New Mexico
12:15 PM Floor Discussion
 
 

177
Mon, 7/30/2018, 10:30 AM - 12:20 PM CC-West 111
Section on Statistical Learning and Data Science CPapers 2 — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Julia Wrobel, Columbia University
10:35 AM Multilinear Low-Rank Vector Autoregressive Modeling via Tensor Decomposition
Presentation
Di Wang, University of Hong Kong; Guodong Li, University of Hong Kong; Dr. LIAN Heng, City University of Hong Kong
10:50 AM Coordinate-Independent Sparse Estimation in Semiparametric Models
Haileab Hilafu, University of Tennessee; Sandra Safo, University of Minnesota
11:05 AM WPSVM for Spatial Point Processes Directed by Gaussian Random Fields
Presentation
Subha Datta, New Jersey Institute of Technology
11:20 AM Capturing Enhanced Information with Higher-Order Tensorian Statistics and Predicting Mortality from Accelerometry-Measured Physical Activity
Presentation
Junrui Di, Johns Hopkins Bloomberg School of Public Health; Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health
11:35 AM Semi-Orthogonal Matrix Factorization
Yutong Li, University of Illinois at Urbana-Champaign; Ruoqing Zhu, University of Illinois Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
11:50 AM Sufficient Dimension Reduction Using Deep Neural Networks
Yixi Xu, Purdue University; Xin Zhang, Florida State University; Xiao Wang , Purdue University
12:05 PM Correlation Tensor Decomposition and Its Application in Spatial Imaging Data
Yujia Deng, UIUC; Xiwei Tang, University of Virginia; Annie Qu, University of Illinois at Urbana-Champaign
 
 

197
Mon, 7/30/2018, 10:30 AM - 11:15 AM CC-West Hall B
SPEED: Government and Health Policy — Contributed Poster Presentations
Health Policy Statistics Section, Government Statistics Section, Section on Statistical Learning and Data Science, Section on Teaching of Statistics in the Health Sciences, Section for Statistical Programmers and Analysts
Chair(s): Paul McNicholas, McMaster University
Oral Presentations for this session.
21: DataSifter: Statistical Obfuscation of Electronic Health Record and Other Sensitive Data Sets
Nina Zhou, University of Michigan; Simeone Marino, Statistics Online Computational Resource, University of Michigan; Lu Wang, University of Michigan; Yiwang Zhou, University of Michigan; Ivo Dinov, Statistics Online Computational Resource, University of Michigan
22: Deep Learning on Small Data - Experiences in Transfer Learning for Healthcare
Dennis Murphree
23: Doing More with Less - Eliminating the Long Survey Forms from the Occupational Employment Statistics Survey
Carrie K. Jones, US Bureau of Labor Statistics
26: Statistically Supporting Health Policy Decision-Making
Frank Yoon, IBM Watson Health
27: Intravenous Fluid Treatments for Ebola Patients: The Risk and the Reward
Derrick Yam, Brown University; Tao Liu, Brown University; Adam Levine, Brown University; Adam Aluisio, Brown University; Shiromi Peters, International Medical Corps; Suzanne Averill, International Medical Corps; Stephen Kennedy, Ministry of Health, Liberia; Fodey Sahr, Sierra Leone Ministry of Defence; Jillian Peters, Brown University; Daniel Cho, Brown University
28: Comparison of Methods for Predicting High-Cost Patients Captured Within the Oncology Care Model (OCM): a Simulation Study
Jung-Yi Lin, Icahn School of Medicine at Mount Sinai; Wei Zhang, UALR; Mark Liu, Mount Sinai Health System; Mark Sanderson, Mount Sinai Health System; Luis Isola, Mount Sinai Health System; Madhu Mazumdar, Icahn School of Medicine at Mount Sinai; Liangyuan Hu, Icahn School of Medicine at Mount Sinai
29: Intervening on the Data to Improve the Performance of Health Plan Payment Methods
Savannah Bergquist, Harvard University; Tim Layton, Harvard Medical School; Tom McGuire, Harvard Medical School; Sherri Rose, Harvard Medical School
30: Developing and Evaluating Methods for Estimating Race/Ethnicity in an Incomplete Dataset Using Address, Surname and Family Race
Gabriella Christine Silva, Brown University; Roee Gutman, Brown University
31: Can Post-Stratification Weighting Eliminate the Need for Additional Weights Adjustments?
Chrishelle Lawrence, U.S. Energy Information Administration
32: Open Data Sharing and Its Statistical Limitations
Pooja Iyer, RTI International; Barbara Do, RTI International
33: Predictors of Hospitalization During a Medicare Skilled Nursing Facility Stay
Fei Han, The Hilltop Institute; Ian Stockwell, The Hilltop Institute
34: Comparison of Treatment Policies Using Bayesian Nonparametric G-Formula
Yizhen Xu, Brown University; Tao Liu, Brown University; Rami Kantor, Brown University; Joseph W Hogan, Brown University School of Public Health
35: Optimal Matching Approaches in Health Policy Evaluations Under Rolling Enrollment
Jonathan Gellar, Mathematica Policy Research; Jiaqi Li, Mathematica Policy Research; Lauren Vollmer, Mathematica Policy Research
36: Assessing Health Care Interventions via an Interrupted Time Series Model: Study Power and Design Considerations
Maricela Cruz, University of California, Irvine; Miriam Bender, University of California, Irvine; Daniel L. Gillen, University of California, Irvine; Hernando Ombao, King Abdullah University of Science and Technology
37: Absence of Evidence Is Not Evidence of Absence: a Better Parallel Trends Test
Alyssa Bilinski, Harvard Graduate School of Arts and Sciences; Laura Hatfield, Harvard Medical School
38: New Applications of Machine Learning to Estimating Large Physician Demand Models
Bryan Sayer, Social & Scientific Systems, Inc.; William Encinosa, Agency for Health Care Quality and Research
39: On Utilizing Published Prevalence Estimates to Perform Difference-In-Difference Tests: Testing the Impact of Recreational Marijuana Laws
Christine Mauro, Columbia University; Chen Chen, New York State Psychiatric Institute; Silvia Martins, Columbia University; Magda Cerdá, University of California, Davis; Melanie M. Wall, Columbia University
40: Community Detection with Dependent Connectivity
Yubai Yuan, University of Illinois at Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
Oral Presentations for this session.
 
 

225 *
Mon, 7/30/2018, 2:00 PM - 3:50 PM CC-West 219
The Interface of Functional Data Analysis and Biomedical Applications — Topic Contributed Papers
Biometrics Section, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics
Organizer(s): Gen Li, Columbia University
Chair(s): Gen Li, Columbia University
2:05 PM Multiple Change Point Detection for Symmetric Positive Definite Matrices
Dehan Kong, University of Toronto; Zhenhua Lin, University of Toronto; Qiang Sun, University of Toronto
2:25 PM Gradient Synchronization to Quantify Brain Functional Connectivity
Jane-Ling Wang, Univ of California-Davis; Yang Zhou, UC Davis; Hans Mueller, UC Davis; Owen Carmichael, Pennington Biomedical Research Center
3:05 PM Manifold Data Analysis with Applications to High-Resolution 3D Imaging
Matthew Reimherr, Pennsylvania State University
3:25 PM A Bootstrap-Based Goodness-of-Fit Test of Covariance for Functional Data
Presentation
Luo Xiao, North Carolina State University; Stephanie Chen, North Carolina State University; Ana-Maria Staicu, NC State University
3:45 PM Floor Discussion
 
 

236
Mon, 7/30/2018, 2:00 PM - 3:50 PM CC-West 212
SLDS Student Paper Awards — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): Todd Ogden, Columbia University
2:05 PM Sparse-Input Neural Networks for High-Dimensional Nonparametric Regression and Classification
Jean Feng; Noah Simon, University of Washington
2:25 PM Valid Inference Corrected for Outlier Removal
Shuxiao Chen, Cornell Univ; Jacob Bien, University of Southern California
2:45 PM Variable Selection for Highly Correlated Predictors
Presentation
Fei Xue, University of Illinois at Urbana-Champaign; Annie Qu, University of Illinois at Urbana-Champaign
3:05 PM PULasso: High-Dimensional Variable Selection with Presence-Only Data
Presentation
Hyebin Song, UW-Madison
3:25 PM Network Augmented Classification
Presentation
Boang Liu, University of Michigan; Ji Zhu, University of Michigan
3:45 PM Floor Discussion
 
 

241
Mon, 7/30/2018, 2:00 PM - 3:50 PM CC-West 210
SLDS CPapers New — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Todd Ogden, Columbia University
2:05 PM Deep Neural Network Model for Predicting Gene Activity Using Three-Dimensional Structures of Chemical Compounds
Presentation
Pingzhao Hu, University of Manitoba; Md. Mohaiminul Islam, University of Manitoba; Kevin Jeffers, University of Manitoba; Andrew M Hogan , University of Manitoba; Rebecca Davis, University of Manitoba; Silvia Cardona, University of Manitoba
2:20 PM A Weighted Learning Approach for Sufficient Dimension Reduction in Binary Classification
Presentation
Seung Jun Shin, Korea University
2:35 PM Prediction on Network-Linked Data by Matrix Variate Models
Xuefei Zhang, University of Michigan; Ji Zhu, University of Michigan
2:50 PM A Cluster Elastic Net for Multivariate Regression
Presentation
Ben Sherwood, University of Kansas; Bradley S Price, West Virginia University
3:20 PM A Scalable Classification Method Based on the Area Under the Receiver Operating Curve
Presentation
Wenyi Wu, University of Michigan; Ji Zhu, University of Michigan
3:35 PM Ensemble of Iterative Classifier Chains for Multi-Label Classification
Presentation
Zhoushanyue He, University of Waterloo; Matthias Schonlau, University of Waterloo
 
 

250
Mon, 7/30/2018, 2:00 PM - 3:50 PM CC-West 111
Topics in Statistical Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Yutao Liu, Columbia University
2:05 PM Predicting Disease Incidence with Natural Cubic Splines
Presentation
Noah Kochanski, Hope College; Yew-Meng Koh, Hope College
2:20 PM Structure Learning for Phylogenetic Tree with Quantitative Characters
Chaoyu Yu; Mathias Drton, University of Washington
2:35 PM Greedy Active Learning Algorithm for Logistic Regression Models
Presentation
Ray-Bing Chen, National Cheng Kung University, Taiwan; Hsiang-Ling Hsu, National University of Kaohsiung; Yuan-Chin Ivan Chang, Academia Sinica
2:50 PM Multilayer Tensor Factorization with Applications to Recommender Systems
Presentation
Xuan Bi; Annie Qu, University of Illinois at Urbana-Champaign; Xiaotong Shen, University of Minnesota
3:05 PM Iterative Quantile Nearest-Neighbors
Presentation
Karsten Maurer, Miami University
3:20 PM Prediction Using Machine Learning Algorithms by Small Sample Size Data
Presentation
Yan Wang, Field School of Public Health, UCLA; Honghu Liu, UCLA; Jian L Zhang, Kaiser Permanente
3:35 PM Floor Discussion
 
 

254
Mon, 7/30/2018, 2:00 PM - 3:50 PM CC-West Hall B
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Paul McNicholas, McMaster University
31: Computing Mean Partition and Assessing Uncertainty for Clustering Analysis
Beomseok Seo, Penn State University; Lin Lin, The Pennsylvania State University; Jia Li, Penn State University
32: A Generalized Fellegi-Sunter Framework for Unsupervised Collective Record Linkage in Clustered Relational Data with Applications to Electronic Health Records
Nicole Solomon, Duke University Medical Center; Sean M O'Brien, Duke University Medical Center; Joseph Lucas, Duke University
33: Predictive Big Data Analytics in Mental Disorders Using the UK Biobank
Yiwang Zhou, University of Michigan; Ivo Dinov, Statistics Online Computational Resource, University of Michigan; Simeone Marino, Statistics Online Computational Resource, University of Michigan
34: Sparse Variable Selection in Kernel Discriminant Analysis via Optimal Scoring
Alexander Lapanowski, Texas A&M; Irina Gaynanova, Texas A&M Univeristy
35: An Application of Clustering Method on EHR Data Phenotyping and Prediction
Shu Wang, University of Pittsburgh; Joyce Chung-Chou H Chang, University of Pittsburgh; Christopher W. Seymour, University of Pittsburgh; Jason Kennedy, University of Pittsburgh; Zhongying Xu, University of Pittsburgh
36: An Algorithm to Compare Patterns and Its Application on Shoe Out-Sole Impressions
Soyoung Park, Iowa State University / CSAFE; Alicia Carriquiry, Iowa State University
37: Predicting Hospital Readmission for Diabetes Patients by Classical and Machine Learning Approaches
Gabrielle LaRosa, University of Pittsburgh; Chathurangi Pathiravsan, Southern Illinois University Carbondale; Rajapaksha Wasala M Anusha Madushani, University of Florida
38: The Classification of Stellar Systems Through Singular Spectrum Analysis
Kevin Matheson, Western Washington University; Kevin Covey, Western Washington University; Kimihiro Noguchi, Western Washington University
39: Machine Learning with Ensemble Feature Selections for Mass Spectrometry Data in Cancer Study
Yulan Liang, University of Maryland Baltimore; Amin Gharipour, Griffith University; Arpad Kelemen, University of Maryland Baltimore; Adam Kelemen, University of Maryland College Park; Hui Zhang, Johns Hopkins Medical Institutions
40: Structured Mixture of Linear Mappings in High Dimension
Chun-Chen Tu, University of Michigan; Florence Forbes, INRIA; Benjamin Lemasson, Universit ´e Grenoble; Naisyin Wang, U of Michigan
44: A Generalization of Convolutional Neural Networks to Graph-Structured Data
Yotam Hechtlinger, Carnegie Mellon Univ; Purvasha Chakravarti, Carnegie Mellon University; Jining Qin, Carnegie Mellon University
45: Empirical Evaluation for Platt Scaling and Isotonic Regression
Weihua Shi, SAS Institute, Inc.
47: Graphical Model for Continuous Longitudinal Data
Lei Wang, The University of Queensland
48: The Application of Elastic Net with Fused Term in Change Point Detection via Coordinate Descent
Zhi Wang, University of Alabama
49: Global Sensitivity Analysis from Given Data : Elementary Effect Approach
Jong hyun Kim, Hanyang University; Dae il Jang, Hanyang University; Kyung joon Cha, Hanyang University
50: Per-Gene Normalization Method (UQ-PgQ2) Improves the Specificity for the Analysis of Differential Gene Expression in RNA-Seq Data
Xiaohong Li, University of Louisville; Nigel G.F. Cooper, University of Louisville; Dongfeng Wu, University of Louisville; Eric C. Rouchka, University of Louisville; Shesh N. Rai, University of Louisville
51: Multivariate Zero-Inflated Poisson Regression
Yang Wang, University of Alabama
52: Sound and Solid Selection of Covariates - a Simulation Study
Kira Dynnes Svendsen, Technical University of Denmark; Nina Munkholt Jakobsen, Technical University of Denmark
53: Machine-Learning Approach to Defining Covariates to Increase Study Power in ALS Clinical Trials and Other Multifactorial Heterogeneous Disease Areas
Danielle Beaulieu, Origent Data Sciences; Albert Taylor, Origent Data Sciences; Samad Jahandideh, Origent Data Sciences; David Ennist, Origent Data Sciences; Andrew Conklin, Origent Data Sciences; Mike Keymer, Origent Data Sciences
54: Functional Graphical Model Classification
Peide Li
55: Variation of Functional Connectome Topology and Its Implications for Attention
Kelson Zawack, Yale University
56: Model-Based Clustering of Time-Dependent Categorical Sequence
Yingying Zhang, The University of Alabama; Volodymyr Melnykov, University of Alabama
57: Learning an Interpretable Behavioral Intervention Policy Using MHealth Data
Xinyu Hu, Columbia University; Min Qian, Columbia University; Ying Kuen Ken Cheung, Columbia University
58: Spatial and Temporal Trends in Weather Forecasting and Improving Predictions with ARIMA Modeling
Manasi Sheth, California State University; Mahalaxmi Gundreddy, California State University East Bay; Vivek Shah, Applied Materials, Inc. ; Pritam Barlota, California State University East Bay; Eric Suess, CSU East Bay
60: Classification Accuracy of Unsupervised Learning Methods with Discrete and Mixture Distributed Indicators: a Monte Carlo Simulation Study
Chi Chang
61: Covariate-Adjusted Tensor Classification in High-Dimensions
Yuqing Pan, Florida State University; Qing Mai, Florida State University; Xin Zhang, Florida State University
 
 

216507
Mon, 7/30/2018, 4:00 PM - 5:30 PM CC-East 18
Section on Statistical Learning and Data Science Business Meeting — Other Cmte/Business
Section on Statistical Learning and Data Science
Chair(s): Cynthia Rudin, Duke University
 
 

274 *
Tue, 7/31/2018, 8:30 AM - 10:20 AM CC-West 110
Random Forests in Big Data, Machine Learning and Statistics — Invited Papers
Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Section on Statistical Computing, SSC
Organizer(s): Ruoqing Zhu, University of Illinois Urbana-Champaign
Chair(s): Yifan Cui, University of North Carolina at Chapel Hill
8:35 AM Standard Errors and Confidence Intervals for Variable Importance in Random Forest Regression, Classification, and Survival
Presentation
Hemant Ishwaran, University of Miami
8:55 AM Random Forests for Big Data
Presentation
Jean-Michel Poggi, LMO, University Paris Sud; Robin Genuer, ISPED, Univ. Bordeaux ; Nathalie Villa-Vialaneix, MIA-T, INRA of Toulouse; Christine Tuleau-Malot , University Nice, CNRS, LJAD
9:15 AM Distributional Trees and Forests
Presentation
Lisa Schlosser, University of Innsbruck; Torsten Hothorn, University of Zurich; Reto Stauffer, University of Innsbruck; Achim Zeileis, University of Innsbruck
9:35 AM Beyond the Bagg: Consistent Importance Intervals for Random Forest Predictors
Lucas Mentch, University of Pittsburgh; Giles Hooker, Cornell University
9:55 AM On the Asymptotics of Tree-Based Survival Models
Presentation
Ruoqing Zhu, University of Illinois Urbana-Champaign; Yifan Cui, University of North Carolina at Chapel Hill; Michael Kosorok, University of North Carolina at Chapel Hill; Mai Zhou, University of Kentucky
10:15 AM Floor Discussion
 
 

280 !
Tue, 7/31/2018, 8:30 AM - 10:20 AM CC-East 19
Leading the Stream: Novel Methods for Streaming Data — Invited Papers
Business and Economic Statistics Section, Section on Statistical Learning and Data Science, Royal Statistical Society
Organizer(s): Idris Eckley, Lancaster University
Chair(s): Hernando Ombao, King Abdullah University of Science and Technology
8:35 AM Automated Bayesian Inference for Large-Scale Datastreams
Presentation
Trevor Campbell, Massachusetts Institute of Technology; Tamara Broderick, Massachusetts Institute of Technology
9:00 AM Sequential Change-Point Detection Based on Nearest Neighbors
Hao Chen, University of California, Davis
9:25 AM Multiscale Models for Continuous Time Interaction Data
Tyler McCormick, University of Washington; Wesley Lee, University of Washington; Rumi Chunara, New York University
9:50 AM Efficient Detection of Anomalies Within Streaming Data
Alexander Fisch, Lancaster University; Idris Eckley, Lancaster University; Paul Fearnhead, Lancaster University
10:15 AM Floor Discussion
 
 

284 * !
Tue, 7/31/2018, 8:30 AM - 10:20 AM CC-West 215/216
So You Think You Can Predict Crime? Lessons Learned from the NIJ Spatiotemporal Crime Forecasting Competition — Invited Panel
Committee on Law and Justice Statistics, Section on Statistical Learning and Data Science
Organizer(s): William Herlands, Carnegie Mellon University; Charles Loeffler, University of Pennsylvania
Chair(s): Charles Loeffler, University of Pennsylvania
8:35 AM Committee on Law and Justice Statistics
Presentation 1 Presentation 2 Presentation 3 Presentation 4 Presentation 5
Panelists: Joel Hunt, National Institute of Justice
Patryk Miziula, deepsense.ai
George Mohler, IUPUI
Tuanjie Tong, Intuidex, Inc.
Dylan Fitzpatrick, Carnegie Mellon University
10:10 AM Floor Discussion
 
 

310
Tue, 7/31/2018, 8:30 AM - 10:20 AM CC-West 111
Topics of Variable Selection — Contributed Papers
Section on Statistical Learning and Data Science, SSC
Chair(s): Haocheng Li, Hoffmann-La Roche Limited (Roche Canada)
8:35 AM Scrutiny of Inference on Generalized Linear Models with High-Dimensional Covariates
Lu Xia, University of Michigan; Bin Nan, University of California, Irvine; Yi Li, University of Michigan
8:50 AM Using Statistical Approaches to Stratify Hospital-Readmission Risk After Hip Fracture
Presentation
Qingqing Dai, Oklahoma State University; Zhuqi Miao, Oklahoma State University; Lan Zhu, Oklahoma State University
9:05 AM Feature Selection in L0 Norm: a Viable Approach
Presentation
Ana Maria Kenney, Pennsylvania State University; Francesca Chiaromonte, The Pennsylvania State University; Giovanni Felici, IIASI CNR
9:20 AM Projection-Based Inference for High-Dimensional Linear Models
Presentation
Sangyoon Yi, Texas A&M Univ; Xianyang Zhang, Texas A&M University
9:35 AM Robust Group LASSO Methods
Presentation
Kristin Lilly, Columbus State University; Nedret Billor, Auburn University
9:50 AM Nonlinear Variable Selection Using Deep Neural Network
Presentation
Yao Chen, Purdue University; Faming Liang, Purdue University; Xiao Wang , Purdue University
10:05 AM Budget-Constrained Feature Selection for Binary Classification: a Neyman-Pearson Approach
Presentation
Yiling Chen, University of California, Los Angeles; Xin Tong, University of Southern California; Jingyi Li, University of California, Los Angeles
 
 

320 * !
Tue, 7/31/2018, 10:30 AM - 12:20 PM CC-West 118
Practical and Realistic Variable Selection Methods — Invited Papers
IMS, Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, SSC
Organizer(s): Linda Zhao, University of Pennsylvania
Chair(s): Sayan Mukherjee, Duke University
10:35 AM Generalized CP and the Bootstrap for Variable Selection in Moderate or High-Dimensional Data
Lawrence D Brown, University of Pennsylvania; Junhui Cai, University of Pennsylvania; Linda Zhao, University of Pennsylvania
11:05 AM Multidimensional Monotonicity Discovery with MBART
Edward George, Wharton, University of Pennsylvania; Robert McCulloch, Arizona State University; Hugh Chipman, Acadia University; Tom Shively, University of Texas at Austin
11:35 AM Statistical Inference for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
Presentation
Weijie Su, University of Pennsylvania; Yuancheng Zhu, University of Pennsylvania
12:05 PM Floor Discussion
 
 

329 * !
Tue, 7/31/2018, 10:30 AM - 12:20 PM CC-West 121
Novel Developments in Functional Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science, Biometrics Section, Section on Nonparametric Statistics
Organizer(s): Andrada E Ivanescu, Montclair State University
Chair(s): Ciprian Crainiceanu, Johns Hopkins University
10:35 AM Registration for Exponential Functional Data
Presentation
Julia Wrobel; Jeff Goldsmith, Columbia University
10:55 AM Outlier Detection in Dynamic Functional Models
Presentation
Andrada E Ivanescu, Montclair State University; William Checkley, Johns Hopkins University; Ciprian Crainiceanu, Johns Hopkins University
11:15 AM Functional Graphical Models for Analyzing Interactions Between Animals
Jan Gertheiss, Clausthal University of Technology
11:35 AM Tidyfun: a New Framework for Representing and Working with Function-Valued Data
Presentation
Fabian Scheipl, LMU Munich; Jeff Goldsmith, Columbia University
11:55 AM Floor Discussion
 
 

341
Tue, 7/31/2018, 10:30 AM - 12:20 PM CC-West 212
SPEED: Classification and Data Science — Contributed Speed
Section on Statistical Learning and Data Science, SSC
Chair(s): Jesse Cambon, Office of Immigration Statistics
Poster Presentations for this session.
10:35 AM Targeted Maximum Likelihood Estimation of Causal Effects Based on Observing a Single Time Series
Ivana Malenica; Mark van der Laan, UC Berkeley
10:40 AM Accessible Statistical Reports in R: Using R, Markdown, and Word to Create Accessible Reproducible Documents
Presentation
Robert Montgomery, NORC; Peter Herman, NORC at the University of Chicago; Qiao Ma, NORC at the University of Chicago; Stephen Schacht, NORC at the University of Chicago
10:45 AM Differentiable Approximations of Hidden Markov Models for Variational Bayesian Inference
Lun Yin, Duke Institute for Brain Sciences; John Pearson, Duke University
10:50 AM How to Effectively Communicate Misunderstood Statistical Terms
Presentation
Hoiyi Ng, Amazon; Paavni Rattan, Amazon
10:55 AM Aggregated Pairwise Classification of Statistical Shapes with Optimal Points of Projection
Presentation
Min Ho Cho, The Ohio State University; Sebastian Kurtek, The Ohio State University; Steve MacEachern, The Ohio State University
11:00 AM Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes Under Possible Non-Proportional Hazards
Presentation
Lauren Spirko, Temple University; Karthik Devarajan, Fox Chase Cancer Center
11:05 AM Improving a Predictive Model of Student Progress in an Online Course by Adding Learned Features from Unstructured Text Data
Huafeng Zhang, The Refugee Center Online
11:10 AM Classification via Product Conditional Density Estimates: Blending LDA and QDA
Presentation
Jiae Kim; Steve MacEachern, The Ohio State University
11:15 AM Comparison of Missing Data Methods in the Use of LASSO Regression for Model Selection with Applications to the National Trauma Data Bank
Presentation
Sarah B Peskoe, Duke University; Tracy Truong, Duke University; Lily R Mundy, Duke University School of Medicine; Ronnie L Shammas, Duke University School of Medicine; Scott T Hollenbeck, Duke University School of Medicine
11:20 AM An Alternative to the Carnegie Classifications: Using Structural Equation Models to Identify Similar Doctoral Institutions
Paul Harmon, Montana State University; Sarah McKnight, Montana State University; Laura Hildreth, Montana State University; Ian C. Godwin, Montana State University Office of Planning and Analysis; Mark Greenwood, Montana State University
11:30 AM Efficient Semiparametric Generalized Linear Models Based on Exponentially Tilted Splines
Presentation
William H Aeberhard, Dalhousie University; Mark Hannay, Intrum Justitia CH
11:35 AM A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
Presentation
David A James, Novartis
11:40 AM A Direct Approach to High-Dimensional Error-In-Variables Regression
Presentation
Yunan Wu, University of Minnesota; Lan Wang, University of Minnesota
11:45 AM A Modified Approach to Component-Wise Gradient Boosting for High-Dimensional Regression Models
Presentation
Brandon Butcher, University of Iowa; Brian J. Smith, University of Iowa
11:50 AM Efficient Big Data Model Selection with Applications to Fraud Detection
Gregory Vaughan, Bentley University
11:55 AM Predicting Overflow: A Novel Application of Latrine Sensors and Machine Learning for Optimizing Sanitation Services in Informal Settlements
Presentation
Phillip Turman-Bryant, Portland State University; Evan Thomas, Portland State University
12:00 PM Undergraduate Data Science Statistics Pathways: What Is Needed for Entry into the Major?
Presentation
Rebecca Hartzler, Charles A. Dana Center, University of Texas at Austin; Nicholas J. Horton, Amherst College
12:05 PM Assessing Divide-and-Conquer Latent Class Analysis
Presentation
Qiao Ma, NORC at the University of Chicago; Meimeizi Zhu, NORC at the University of Chicago; Edward Mulrow, NORC at the University of Chicago
12:10 PM Lookalike Audience Modeling
Sam Hawala, Resonate-Networks
 
 

342
Tue, 7/31/2018, 10:30 AM - 12:20 PM CC-West 209
SPEED: Sports to Fire: Fascinating Applications of Statistics — Contributed Speed
Section on Statistics in Sports, SSC, Section on Statistics in Imaging, Section on Statistical Computing, Section on Statistical Consulting, Section on Statistical Learning and Data Science, Section on Statistics in Epidemiology, Statistical Auditing Interest Group, Transportation Statistics Interest Group, Section on Teaching of Statistics in the Health Sciences, Section for Statistical Programmers and Analysts
Chair(s): Bo Chen, University of Toronto
Poster Presentations for this session.
10:35 AM Claim-Level Models Using Statistical Learning Techniques and Risk Analysis
Presentation
Mathieu Pigeon, Université du Québec à Montréal; Francis Duval, Université du Québec à Montréal
10:40 AM Beach Volleyball Team Optimization
Presentation
Matthew Oehler, BYU
10:45 AM Distributions of Time to First Spot Fire
Presentation
Trevor Thomson, Simon Fraser University
10:50 AM Rao-Blackwellizing Field Goal Percentage in the NBA
Presentation
Daniel Daly-Grafstein, Simon Fraser University; Luke Bornn, Sacramento Kings and Simon Fraser University
10:55 AM Estimating Attendance at Non-Ticketed Non-Gated Events
Presentation
Carl Schwarz, Simon Fraser University
11:10 AM Teaching Statistics Graduate Students the Importance of Reproducible Research
Presentation
Kristen McQuerry, University of Kentucky
11:15 AM Shared and Study-Specific Dietary Patterns: a Novel Approach to Replicability and Validity
Roberta De Vito; Carlo La Vecchia, Università degli Studi di Milano; Giovanni Parmigiani , Harvard T.H. Chan School of Public Health / Dana-Farber Cancer Institute; Valeria Edefonti, Università degli Studi di Milano
11:20 AM Statistical Ethics and Challenging Substantial Errors in Statistical Methods and Results in a Prominent Peer Reviewed Economics Journal
Chris Barker, Statistical Planning and Analysis Services, Inc.
11:30 AM To Bet or Not to Bet - the Modified Kelly Criteria
Presentation
Dani Chu, SFU Sports Analytics Club; Yifan Wu, Simon Fraser University; Tim Swartz, Simon Fraser University
11:35 AM The Home Run Spike of MLB 2017: Drop in Quality of Pitch (QOP) Is a Missing Factor
Presentation
Jason Wilson, Biola University
11:40 AM An Application of Machine Learning for 3D IC Defect Detection
Presentation
Meihui Guo, National Sun Yat-Sen University; Yu-Jung Huang, I-Shou University
11:50 AM The Simple Story of Advanced NBA Metrics
Zach Fulker, University of Pittsburgh; Tyler Folta, University of Pittsburgh; Lucas Mentch, University of Pittsburgh
11:55 AM Application of Email Spam Filtering Algorithms to SMS Data
Presentation
Yishu Xue, University Of Connecticut
12:00 PM MLB Rule IV Draft: Valuing Draft Pick Slots
Presentation
Anthony Cacchione, City College of New York
12:05 PM Assessing the Impact of Practice Restriction Rules on Injury Rates in the National Football League (NFL)
Presentation
Zachary Binney, Rollins School of Public Health, Emory University; Cecile Janssens, Rollins School of Public Health, Emory University; Kyle E Hammond, Emory University School of Medicine; Mitchel Klein, Rollins School of Public Health, Emory University; Michael Goodman, Emory University
 
 

354
Tue, 7/31/2018, 10:30 AM - 12:20 PM CC-West 119
Topics in Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Todd Ogden, Columbia University
10:35 AM Using Q-Learning Method in Identify Optimal Treatment Regime
Presentation
Haocheng Li, Hoffmann-La Roche Limited (Roche Canada); Vincent Shen, Hoffmann-La Roche Limited (Roche Canada); Hao Xu, Hoffmann-La Roche Limited (Roche Canada); Sylvia Hu, Roche-Genentech
10:50 AM High-Dimensional Sparse Generalized Eigenvalue Problem and Its Applications to Multivariate Statistics
Presentation
Kean Ming Tan, University of Minnesota; Zhaoran Wang, Northwestern University; Han Liu, Northwestern University; Tong Zhang, Tencent Technology
11:05 AM Personalized Solution Recommendation for Google Cloud Marketplace
Tianhong He, Google; Sangho Yoon, Google
11:20 AM Statistical Modeling for Pooling and Analyzing Multi-Site Data Sets Using Maximum Mean Discrepancy
Hao Zhou, University of Wisconsin Madison
11:35 AM Model-Based Electronic Health Records Phenotyping from Only Positive and Unlabeled Data
Presentation
Lingjiao Zhang, University of Pennsylvania; Naveen Muthu, University of Pennsylvania; Xiruo Ding, University of Pennsylvania; Daniel S Herman, University of Pennsylvania; Jinbo Chen, University of Pennsylvania
11:50 AM Structure and Sensitivity in Differential Privacy: Comparing K-Norm Mechanisms
Presentation
Jordan Alexander Awan, Pennsylvania State University; Aleksandra Slavkovic, Pennsylvania State University
12:05 PM Optimization Over Nonconvex Constraints
Presentation
Wooseok Ha; Rina Foygel Barber, University of Chicago
 
 

381 !
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-West 212
High-Dimensional Nonparametric Statistics — Invited Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, IMS, SSC
Organizer(s): Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
Chair(s): Lan Wang, University of Minnesota
2:05 PM High-Dimensional Sign Tests for the Direction of a Skewed Single-Spiked Distribution
Presentation
Davy Paindaveine, Université libre de Bruxelles; Thomas Verdebout, Université libre de Bruxelles
2:30 PM Robust Estimation, Efficiency, and Lasso Debiasing
Presentation
Po-Ling Loh, UW-Madison
3:20 PM MASES: a Nonparametric Dimension Reduction Approach
Hui Zou, University of Minnesota; Qing Mai, Florida State University; Xin Zhang, Florida State University
3:45 PM Floor Discussion
 
 

387
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-West 213
Foundations of Data Science — Invited Papers
IMS, Section on Statistical Learning and Data Science, Royal Statistical Society, SSC
Organizer(s): Sofia C Olhede, University College London
Chair(s): Sofia C Olhede, University College London
2:05 PM A Statistical View on Optimal Transport: Inference, Algorithms, Applications
Axel Munk, University of Goettingen; Joern Schrieber, Department for Mathematics and Computer Science; Max Sommerfeld, Department for Mathematics and Computer Science; Carla Tameling, Department for Mathematics and Computer Science
2:30 PM Large Numbers of Explanatory Variables
Heather Battey, Imperial College London; David Cox, Nuffield College
2:55 PM A Fast Algorithm with Minimax Optimal Guarantees for Topic Models with an Unknown Number of Topics
Presentation
Florentina Bunea, Cornell Univeristy
3:20 PM Discussant: Patrick J Wolfe, Purdue University
3:45 PM Floor Discussion
 
 

390 * !
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-West Ballroom A
Accessing Resources from the Web in Data Analysis — Invited Papers
Section on Statistical Computing, Section for Statistical Programmers and Analysts, Section on Statistical Learning and Data Science
Organizer(s): Jennifer Bryan, RStudio, University of British Columbia
Chair(s): Jennifer Bryan, RStudio, University of British Columbia
2:05 PM Harnessing the Power of the Web via R Clients for Web APIs
Lucy D'Agostino McGowan, Vanderbilt University
2:30 PM What You Can, Can't, and Shouldn't Do with Social Media Data
Presentation
Rachael Tatman, --
2:55 PM Writing Useful and Maintainable Client Libraries
Craig Citro, Google, Inc
3:20 PM Harnessing the Power of Open Data on the Web
Presentation
Karthik Ram, University of California, Berkeley; Scott Chamberlain, University of California, Berkeley
3:45 PM Floor Discussion
 
 

393 * !
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-West 301
A Life Cycle View of Statistics — Invited Panel
Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Section on Statistical Consulting, SSC
Organizer(s): David Steinberg, Tel Aviv University
Chair(s): David Steinberg, Tel Aviv University
2:05 PM A Life Cycle View of Statistics
Presentation 1 Presentation 2 Presentation 3
Panelists: Laura Freeman, Institute for Defense Analysis
Ron S Kenett, KPA Group
John Peterson, Glaxo-Smith-Kline
Agus Sudjianto, Wells Fargo
3:40 PM Floor Discussion
 
 

396 * !
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-West 117
Field to Fork: Leading with Statistics in the Food Industry — Topic Contributed Papers
Quality and Productivity Section, Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science
Organizer(s): Shankang Qu, PepsiCo
Chair(s): Richard De Veaux, Williams College
2:05 PM Multivariate Analysis of Sensory and Consumer Data
Presentation
Jianfeng Ding, SAS Institute
2:25 PM Accelerating Product Development with Virtual Experimentation
Fred Hulting, General Mills, Inc.
2:45 PM Development of a Processed Cheese Food Safety Model Using a Response Surface Design and Parametric Survival Modeling
Presentation
Francis Rossi, PepsiCo
3:05 PM A Bayesian Hierarchical Model for Integrated Analysis of Consumer Appeal in Affective Testing
Presentation
Jing Cao; Janette Pool, PepsiCo
3:25 PM Floor Discussion
 
 

397 * !
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-East 19
Statistical Learning for Epigenomics Data — Topic Contributed Papers
SSC, Section on Statistics in Genomics and Genetics, Section on Statistical Learning and Data Science
Organizer(s): Michael M. Hoffman, Princess Margaret Cancer Centre/University of Toronto
Chair(s): Pingzhao Hu, University of Manitoba
2:05 PM Inference of Transcription Factor Binding Sites in New Cell Types from Open Chromatin and Gene Expression Data
Michael M. Hoffman, Princess Margaret Cancer Centre/University of Toronto; Mehran Karimzadeh, University of Toronto
2:25 PM Detecting Developmental Expression Switches from Transcriptomic and Epigenomic Data
Presentation
Claudia Kleinman, McGill University; Marie Forest, Lady Davis Research Institute, McGill University; Selin Jessa, McGill University; Celia M.T. Greenwood, Lady Davis Research Institute, McGill University
2:45 PM Inferring the Impact of Genetic Variation on Regulatory Networks
Sara Mostafavi
3:05 PM Understanding Gene Regulation Through Graph-Based Posterior Regularization in Structured Probabilistic Models
Presentation
Maxwell Libbrecht, Simon Fraser University
3:25 PM A Smoothed EM-Algorithm for Modeling DNA Methylation Profiles from Bisulfite Sequencing Data
Presentation
Karim Oualkacha, Universite Du Quebec a Montreal; Celia M.T. Greenwood, Lady Davis Research Institute, McGill University; Kaiqiong Zhao, Epidemiology, Biostatistics and Occupational Health, and Human Genetics, McGill University; Lajmi Lakhal-Chaieb, Université Laval
3:45 PM Floor Discussion
 
 

422
Tue, 7/31/2018, 2:00 PM - 3:50 PM CC-West 223
Statistical Learning for Functional Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Tianhong He, Google
2:05 PM Probabilistic K-Mean with Local Alignment for Functional Motif Discovery
Presentation
Marzia A Cremona, The Pennsylvania State University; Francesca Chiaromonte, The Pennsylvania State University
2:20 PM Multivariate Calibration with Robust Signal Regression
Presentation
Bin Li, Louisiana State University ; Brian D. Marx, Louisiana State University; David C Weindorf, Texas Tech University; Somsubhra Chakraborty, Indian Institute of Technology Kharagpur
2:35 PM Statistical Methods for Wearable Device Data with an Application in Clinical Studies
Presentation
Xinyue Li, Yale University; Hongyu Zhao, Yale; Michael John Kane, Yale University; Yunting Zhang, Shanghai Children's Medical Center; Fan Jiang, Shanghai Children's Medical Center; Qingmin Lin, Shanghai Children's Medical Center; Qi Zhu, Shanghai Children's Medical Center; Yuanjin Song, Shanghai Children's Medical Center
2:50 PM Quantifying Genetic Influences on Physical Activity Among Twins Based on Minute-Level Accelerometry Data Among Twins
Presentation
Haochang Shou, University of Pennsylvania; Joanne Carpenter, University of Sydney; Kathleen Merikangas, National Institute of Mental Health; Ian Hickie, University of Sydney
3:05 PM Regression Based Circular Error Probable: An Application to Ballistic Systems
Presentation
Zachary Zimmer; Casey Turner, ATEC
3:20 PM Floor Discussion
 
 

434
Tue, 7/31/2018, 2:00 PM - 2:45 PM CC-West Hall B
SPEED: Classification and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science, SSC
Chair(s): Paul McNicholas, McMaster University
Oral Presentations for this session.
21: Targeted Maximum Likelihood Estimation of Causal Effects Based on Observing a Single Time Series
Ivana Malenica; Mark van der Laan, UC Berkeley
22: Accessible Statistical Reports in R: Using R, Markdown, and Word to Create Accessible Reproducible Documents
Robert Montgomery, NORC; Peter Herman, NORC at the University of Chicago; Qiao Ma, NORC at the University of Chicago; Stephen Schacht, NORC at the University of Chicago
23: Differentiable Approximations of Hidden Markov Models for Variational Bayesian Inference
Lun Yin, Duke Institute for Brain Sciences; John Pearson, Duke University
24: How to Effectively Communicate Misunderstood Statistical Terms
Hoiyi Ng, Amazon; Paavni Rattan, Amazon
25: Aggregated Pairwise Classification of Statistical Shapes with Optimal Points of Projection
Min Ho Cho, The Ohio State University; Sebastian Kurtek, The Ohio State University; Steve MacEachern, The Ohio State University
26: Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes Under Possible Non-Proportional Hazards
Lauren Spirko, Temple University; Karthik Devarajan, Fox Chase Cancer Center
27: Improving a Predictive Model of Student Progress in an Online Course by Adding Learned Features from Unstructured Text Data
Huafeng Zhang, The Refugee Center Online
28: Classification via Product Conditional Density Estimates: Blending LDA and QDA
Jiae Kim; Steve MacEachern, The Ohio State University
29: Comparison of Missing Data Methods in the Use of LASSO Regression for Model Selection with Applications to the National Trauma Data Bank
Sarah B Peskoe, Duke University; Tracy Truong, Duke University; Lily R Mundy, Duke University School of Medicine; Ronnie L Shammas, Duke University School of Medicine; Scott T Hollenbeck, Duke University School of Medicine
30: An Alternative to the Carnegie Classifications: Using Structural Equation Models to Identify Similar Doctoral Institutions
Paul Harmon, Montana State University; Sarah McKnight, Montana State University; Laura Hildreth, Montana State University; Ian C. Godwin, Montana State University Office of Planning and Analysis; Mark Greenwood, Montana State University
31: Efficient Semiparametric Generalized Linear Models Based on Exponentially Tilted Splines
William H Aeberhard, Dalhousie University; Mark Hannay, Intrum Justitia CH
32: A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis
33: A Direct Approach to High-Dimensional Error-In-Variables Regression
Yunan Wu, University of Minnesota; Lan Wang, University of Minnesota
34: A Modified Approach to Component-Wise Gradient Boosting for High-Dimensional Regression Models
Brandon Butcher, University of Iowa; Brian J. Smith, University of Iowa
35: Efficient Big Data Model Selection with Applications to Fraud Detection
Gregory Vaughan, Bentley University
36: Predicting Overflow: A Novel Application of Latrine Sensors and Machine Learning for Optimizing Sanitation Services in Informal Settlements
Phillip Turman-Bryant, Portland State University; Evan Thomas, Portland State University
37: Undergraduate Data Science Statistics Pathways: What Is Needed for Entry into the Major?
Rebecca Hartzler, Charles A. Dana Center, University of Texas at Austin; Nicholas J. Horton, Amherst College
38: Assessing Divide-and-Conquer Latent Class Analysis
Qiao Ma, NORC at the University of Chicago; Meimeizi Zhu, NORC at the University of Chicago; Edward Mulrow, NORC at the University of Chicago
39: Lookalike Audience Modeling
Sam Hawala, Resonate-Networks
Oral Presentations for this session.
 
 

435
Tue, 7/31/2018, 3:05 PM - 3:50 PM CC-West Hall B
SPEED: Sports to Fire: Fascinating Applications of Statistics — Contributed Poster Presentations
Section on Statistics in Sports, SSC, Section on Statistics in Imaging, Section on Statistical Computing, Section on Statistical Consulting, Section on Statistical Learning and Data Science, Section on Statistics in Epidemiology, Statistical Auditing Interest Group, Transportation Statistics Interest Group, Section on Teaching of Statistics in the Health Sciences, Section for Statistical Programmers and Analysts
Chair(s): Paul McNicholas, McMaster University
Oral Presentations for this session.
1: Claim-Level Models Using Statistical Learning Techniques and Risk Analysis
Mathieu Pigeon, Université du Québec à Montréal; Francis Duval, Université du Québec à Montréal
2: Beach Volleyball Team Optimization
Matthew Oehler, BYU
3: Distributions of Time to First Spot Fire
Trevor Thomson, Simon Fraser University
4: Rao-Blackwellizing Field Goal Percentage in the NBA
Daniel Daly-Grafstein, Simon Fraser University; Luke Bornn, Sacramento Kings and Simon Fraser University
5: Estimating Attendance at Non-Ticketed Non-Gated Events
Carl Schwarz, Simon Fraser University
8: Teaching Statistics Graduate Students the Importance of Reproducible Research
Kristen McQuerry, University of Kentucky
9: Shared and Study-Specific Dietary Patterns: a Novel Approach to Replicability and Validity
Roberta De Vito; Carlo La Vecchia, Università degli Studi di Milano; Giovanni Parmigiani , Harvard T.H. Chan School of Public Health / Dana-Farber Cancer Institute; Valeria Edefonti, Università degli Studi di Milano
10: Statistical Ethics and Challenging Substantial Errors in Statistical Methods and Results in a Prominent Peer Reviewed Economics Journal
Chris Barker, Statistical Planning and Analysis Services, Inc.
11: To Bet or Not to Bet - the Modified Kelly Criteria
Dani Chu, SFU Sports Analytics Club; Yifan Wu, Simon Fraser University; Tim Swartz, Simon Fraser University
12: The Home Run Spike of MLB 2017: Drop in Quality of Pitch (QOP) Is a Missing Factor
Jason Wilson, Biola University
13: An Application of Machine Learning for 3D IC Defect Detection
Meihui Guo, National Sun Yat-Sen University; Yu-Jung Huang, I-Shou University
15: The Simple Story of Advanced NBA Metrics
Zach Fulker, University of Pittsburgh; Tyler Folta, University of Pittsburgh; Lucas Mentch, University of Pittsburgh
16: Application of Email Spam Filtering Algorithms to SMS Data
Yishu Xue, University Of Connecticut
17: MLB Rule IV Draft: Valuing Draft Pick Slots
Anthony Cacchione, City College of New York
18: Assessing the Impact of Practice Restriction Rules on Injury Rates in the National Football League (NFL)
Zachary Binney, Rollins School of Public Health, Emory University; Cecile Janssens, Rollins School of Public Health, Emory University; Kyle E Hammond, Emory University School of Medicine; Mitchel Klein, Rollins School of Public Health, Emory University; Michael Goodman, Emory University
Oral Presentations for this session.
 
 

Register 445
Wed, 8/1/2018, 7:00 AM - 8:15 AM CC-West Ballroom D
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): Ali Shojaie, University of Washington
WL07: Data Science in Marketing Research
Chen Teel, Electronic Arts
 
 

451 * !
Wed, 8/1/2018, 8:30 AM - 10:20 AM CC-West 301
Getting Shots Inside the Box-Cox -- Transformational Soccer Analytics — Invited Papers
Section on Statistics in Sports, Significance Magazine, Section on Statistical Learning and Data Science
Organizer(s): Luke Bornn, Sacramento Kings and Simon Fraser University
Chair(s): Dan Cervone, LA Dodgers
8:35 AM Interpretable Analysis of Team Performance in Soccer Using Tracking Data: a Hybrid of Supervised and Unsupervised Methods.
Presentation
Paul David Power, STATS
9:00 AM From Intuition to Objective Analysis: Data-Oriented Strategies at F.C. Barcelona
Presentation
Javier Eduardo Fernández, F.C. Barcelona
9:25 AM Data Science with Your Hair on Fire: Applied Research in Soccer
Presentation
Ted Knutson, StatsBomb Services
9:50 AM Discussant: Luke Bornn, Sacramento Kings and Simon Fraser University
10:15 AM Floor Discussion
 
 

453 !
Wed, 8/1/2018, 8:30 AM - 10:20 AM CC-West 215/216
Novel Theory and Methods in Big Data Analytics — Invited Papers
Section on Statistical Learning and Data Science, Section on Physical and Engineering Sciences, Section on Statistical Computing, SSC
Organizer(s): Ping Ma, University of Georgia
Chair(s): Ping Ma, University of Georgia
8:35 AM Statistical Inference for Big Data via Optimal Subsampling
Presentation
HaiYing Wang, University of Connecticut
9:00 AM Statistical Leverage and Its Usage in Variable Screening
Wenxuan Zhong, University of Georgia; Yiwen Liu, University of Georgia; Peng Zeng, Auburn University
9:25 AM Complex Interaction Modeling with Liquid Association
Presentation
Ker-Chau Li, Institute of Statistical Science, Academia Sinica
9:50 AM Iterative Random Forests (IRF) to Discover Predictive and Stable High-Order Interactions
Bin Yu, UC Berkeley; Sumanta Basu, Cornell University; Karl Kumbier, UC Berkeley; Ben Brown, LBNL and University of Birmingham
10:15 AM Floor Discussion
 
 

455 * !
Wed, 8/1/2018, 8:30 AM - 10:20 AM CC-East 16
Recent Advances in Multiple Graph Inference — Invited Papers
IMS, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics
Organizer(s): Vince Lyzinski, University of Massachusetts Amherst; Daniel L Sussman, Boston University
Chair(s): Daniel L Sussman, Boston University
8:35 AM Omnibus Embeddings for Mutliple Graph Inference
Presentation
Avanti Athreya, Johns Hopkins University; Keith Levin, University of Michigan; Minh Tang, Johns Hopkins University; Carey E Priebe, Johns Hopkins University; Vince Lyzinski, University of Massachusetts Amherst
9:00 AM Graph Matching and Subsequent Inference in Errorfully Observed Network Data
Vince Lyzinski, University of Massachusetts Amherst
9:25 AM Scalable Bayes Inference on Big Dependent Networks
Presentation
David B Dunson, Duke University
9:50 AM Simultaneous Prediction and Community Detection on Networks with Application to Neuroimaging
Jesús Arroyo , University of Michigan; Elizaveta Levina, University of Michigan
10:15 AM Floor Discussion
 
 

463 * !
Wed, 8/1/2018, 8:30 AM - 10:20 AM CC-West 206/207
Novel Uses of Text Analysis in Government Agencies — Topic Contributed Papers
Government Statistics Section, Business and Economic Statistics Section, Section on Statistical Learning and Data Science
Organizer(s): Wendy L Martinez, Bureau of Labor Statistics
Chair(s): Terrance Savitsky, Bureau of Labor Statistics
8:35 AM Identifying Misclassifications in Consumer Expenditure Data
Presentation
Clayton Knappenberger, U.S. Bureau of Labor Statistics
8:55 AM Automatically Generating News Release Statements from Structured Data
Brandon Kopp, Bureau of Labor Statistics
9:15 AM The CFR Miner: Natural Language Processing of the Code of Federal Regulations Using R Studio and Shiny
Presentation
Richard Schwinn, U.S. Small Business Administration
9:35 AM Towards Automated Boilerplate Detection
Presentation
Marco Enriquez, US Securities & Exchange Comm
9:55 AM Discussant: E. James Harner, West Virginia University
10:15 AM Floor Discussion
 
 

487
Wed, 8/1/2018, 8:30 AM - 10:20 AM CC-West 217
Neural Networks, Deep Learning, and RKHS — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Hokwon Cho, University of Nevada, Las Vegas
8:35 AM Reproducing Kernels for Pairwise Learning
Presentation
Xin Guo, The Hong Kong Polytechnic University; Ting Hu, Wuhan University; Qiang Wu, Middle Tennessee State University; Ding-Xuan Zhou, City University of Hong Kong
9:05 AM Folded Concave Penalized Estimation of Conditional Copula Graphical Models with Application to Microbial Networks
Presentation
Bingyuan Liu, Pennsylvania State University; Lingzhou Xue, Penn State University and National Institute of Statistical Sciences
9:20 AM Deep Learning in Medical Imaging: Evaluation and Study Design
Presentation
Robyn Ball, Stanford University; David Larson, Stanford University; Pranav Rajpurkar, Stanford University; Matthew Chen, Nines AI; Jeremy Irvin, Stanford University; Jaden Yang, Stanford University; Matthew P Lungren, Stanford University
9:35 AM Heterogeneous Treatment Effect Estimation through Deep Learning
Presentation
Ran Chen, Wharton; Hanzhong Liu, Center for Statistical Science, Tsinghua University
9:50 AM A Simulation Study on the Performance of Deep Learning Methods for Multi-Category Classification
Dawei Liu, Biogen; Ih Chang, Biogen
10:05 AM Neural Network with Spline Smoothing and Its Applications to Genetics
Presentation
Pei Geng, Illinois State University; Shan Zhang, Michigan State University; Qing Lu, Michigan State University
 
 

495 * !
Wed, 8/1/2018, 10:30 AM - 12:20 PM CC-West 224
The Potential for Web-Scraping in the Production of Official Statistics: An Opportunity for Statistics to Lead? — Invited Papers
Government Statistics Section, Survey Research Methods Section, Section on Statistical Learning and Data Science, Social Statistics Section
Organizer(s): Linda J Young, USDA National Agricultural Statistics Service
Chair(s): Michael Hyman, USDA-NASS
10:35 AM Modernizing Census Bureau Economic Statistics through Web Scraping
Presentation
Brian Dumbacher, U.S. Census Bureau; Carma Ray Hogue, U.S. Census Bureau
11:00 AM The Potential for Web-Scraping in the Production of Official Statistics: An Opportunity for Statistics to Lead?
Presentation
Linda J Young, USDA National Agricultural Statistics Service
11:25 AM Modernizing Government Statistics While Preserving Principles
Presentation
Robert Sivinski, Office of Management and Budget; Rochelle (Shelly) Wilkie Martinez, Office of Management and Budget
11:50 AM Floor Discussion
 
 

496 * !
Wed, 8/1/2018, 10:30 AM - 12:20 PM CC-West 215/216
Building a Computing Age #StatisticsCurriculum for Biomedical Scientists — Invited Papers
Section on Teaching of Statistics in the Health Sciences, Section on Statistical Learning and Data Science, Section on Statistical Education
Organizer(s): Sujata M Patil, Memorial Sloan Kettering Cancer Center
Chair(s): Jaya M Satagopan, Memorial Sloan Kettering Cancer Center
10:35 AM A Guide to Teaching Data Science
Presentation
Rafael Irizarry, Harvard University
10:55 AM Building and Teaching a Statistics Curriculum for Post-Doctoral Biomedical Scientists at a Free-Standing Cancer Center
Presentation
Sujata M Patil, Memorial Sloan Kettering Cancer Center; Ushma Neill, Memorial Sloan Kettering Cancer Center; Jaya M Satagopan, Memorial Sloan Kettering Cancer Center
11:15 AM Experiences with Teaching Genomic Data Science Online
Kasper Daniel Hansen, Johns Hopkins University
11:35 AM Teaching Statistics to Basic Scientists: #KnowYourAudience
Presentation
Stacey J Winham, Division of Biomedical Statistics and Informatics, Mayo Clinic; Natasa Milic, University of Belgrade; Tracey L Weissgerber, Division of Nephrology and Hypertension, Mayo Clinic
11:55 AM Discussant: Naomi S Altman, Pennsylvania State University
12:15 PM Floor Discussion
 
 

507 * !
Wed, 8/1/2018, 10:30 AM - 12:20 PM CC-East 14
Bayesian Data Science and Statistical Science — Topic Contributed Papers
Section on Bayesian Statistical Science, International Society for Bayesian Analysis (ISBA), Section on Statistical Learning and Data Science, SSC
Organizer(s): Tamara Broderick, Massachusetts Institute of Technology
Chair(s): Rajesh Ranganath, NYU Courant Institute of Mathematical Science
10:35 AM Polynomial Approximate Sufficient Statistics for Scalable Bayesian Inference
Presentation
Tamara Broderick, Massachusetts Institute of Technology
10:55 AM Robust Clustering Using Power Posteriors: Calibration and Inference
Presentation
Jeffrey Miller, Harvard School of Public Health; David B Dunson, Duke University
11:15 AM Inferring Social Structure from Continuous-Time Interaction Data
Bailey Fosdick, Colorado State University; Wesley Lee, University of Washington; Tyler McCormick, University of Washington
11:35 AM Probabilistic Programming with Non-Parametric Bayesian Model Discovery in BayesDB
Presentation
Vikash Mansinghka; Feras Saad, MIT
11:55 AM Discussant: Nicholas Foti, University of Washington
12:15 PM Floor Discussion
 
 

509 * !
Wed, 8/1/2018, 10:30 AM - 12:20 PM CC-West 203
New Approaches to Modeling and Inference for Complex Space-Time Data — Topic Contributed Papers
Section on Physical and Engineering Sciences, Section on Statistical Learning and Data Science, Section on Statistics and the Environment, Quality and Productivity Section
Organizer(s): Ta-Hsin Li, IBM T. J. Watson Research Center
Chair(s): Hakmook Kang, Vanderbilt
10:35 AM Testing One Hypothesis Multiple Times: The Multidimensional Case
Sara Algeri, Imperial College London; David A van Dyk, Imperial College London
10:55 AM A Scalable Multi-Resolution Spatio-Temporal Model for Brain Activation and Connectivity in fMRI Data
Presentation
Stefano Castruccio, University of Notre Dame; Hernando Ombao, King Abdullah University of Science and Technology; Marc G Genton, King Abdullah University of Science and Technology
11:15 AM Flexible Dynamic Modeling of Correlation and Covariance Matrices for Spatio-Temporal Data Analysis
Presentation
Babak Shahbaba, UCI; Andrew James Holbrook, UC Irvine; Gabriel Elias, UC Irvine; Norbert J. Fortin, UC Irvine; Hernando Ombao, UC Irvine; Shiweil Lan, CalTech
11:35 AM Automatic Anomaly Detection in Modeling Real-Time Sensor Data
Bei Chen, IBM Research; Beat Buesser, IBM Research
11:55 AM Identification of Management Zone Using a Spatial Clustering Time-Varying Lattice Models
Presentation
Youngdeok Hwang, Sungkyunkwan University; Huijing Jiang, IBM Research; Rodrigue Ngueyep, IBM Research
12:15 PM Floor Discussion
 
 

528
Wed, 8/1/2018, 10:30 AM - 12:20 PM CC-West 119
Analysis of Big Data — Contributed Papers
Section on Statistical Learning and Data Science, SSC
Chair(s): Elizabeth Chou, National Chengchi University
10:35 AM Hybridized Threshold Clustering for Massive Data
Presentation
Jianmei Luo, KANSAS STATE UNIVERSITY; Michael Higgins, KANSAS STATE UNIVERSITY; William Henry Hsu, KANSAS STATE UNIVERSITY; ChandraVyas Annakula, KANSAS STATE UNIVERSITY; Aruna Sai Kannamareddy, KANSAS STATE UNIVERSITY
10:50 AM Fusion Learning with High-Dimensionality
Presentation
Xin Gao, York University; Raymond J. Carroll, Texas A & M University
11:05 AM High-Dimensional Regression for Microbiome Compositional Data
Presentation
Xiaohan Yan, Cornell University; Jacob Bien, University of Southern California; Christian Mueller, Flatiron Institute
11:35 AM Penalized Jackknife Empirical Likelihood in High Dimension
Presentation
Na Zhao; jinfeng Xu, The University of Hong Kong
11:50 AM Correct Model Selection in Big Data Analyzes
Katherine Thompson, University of Kentucky
12:05 PM Floor Discussion
 
 

529
Wed, 8/1/2018, 10:30 AM - 12:20 PM CC-West 117
Regression Trees and Random Forests — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Dawei Liu, Biogen
10:35 AM Regression Trees and Ensemble Methods for Multivariate Outcomes
Presentation
Evan Reynolds, University of Michigan; Mousumi Banerjee, University of Michigan
10:50 AM Repeated Measures Random Forests: Identifying Factors Associated with Nocturnal Hypoglycemia
Presentation
Juanjuan Fan, San Diego State University; Peter Calhoun, Dexcom, Inc.; Richard Levine, San Diego State University
11:05 AM Uniformity of Personalized Treatment
Georgiy Bobashev, Research Triangle Institute; Barry Eggleston, RTI International; Benjamin Carper, RTI International
11:20 AM Locally Linear Forests: Leveraging Smoothness with Random Forests
Presentation
Rina Friedberg, Stanford University; Julie Tibshirani, Palantir Technologies; Susan Athey, Stanford University; Stefan Wager, Stanford University
11:35 AM Conditional Quantile Regression Tree/Random Forest
Huichen Zhu; Ying Wei, Columbia University
11:50 AM Spectral Clustering via Unsupervised Random Forests
Presentation
William Biscarri, University of Illinois at Urbana-Champaign; Robert J. Brunner, University of Illinois at Urbana-Champaign; Ruoqing Zhu, University of Illinois Urbana-Champaign
12:05 PM Assessing Authorship of Beatles Songs from Musical Content: Bayesian Classification Modeling from Bags-Of-Words Representations
Mark Glickman, Harvard University; Jason Brown, Dept of Mathematics, Dalhousie University; Ryan Song, School of Engineering and Applied Science, Harvard University
 
 

533
Wed, 8/1/2018, 10:30 AM - 12:20 PM CC-West 120
SLDS CPapers NEW 2 — Contributed Papers
Section on Statistical Learning and Data Science, Section on Statistical Consulting
Chair(s): Xin Guo, The Hong Kong Polytechnic University
10:35 AM Manifold Learning for Network Inference
Presentation
Mingyue Gao, The Johns Hopkins University; Carey E Priebe, Johns Hopkins University; Minh Tang, Johns Hopkins University
10:50 AM Real-World Learning Analytics: Modeling Student Academic Practices and Performance
Presentation
Chantal D. Larose, Eastern Connecticut State University; Kim Y. Ward, Eastern Connecticut State University
11:05 AM Big Data, Google, and Infectious Disease Prediction: a Statistical Perspective
Presentation
Shihao Yang; S. C. Kou, Harvard University; Mauricio Santillana, Harvard University
11:20 AM Time-Constrained Predictive Modeling on Large and Continuously Updating Financial Data Sets
Presentation
Bernard Lee, HedgeSPA Limited; Nicos Christofides, Imperial College London
11:35 AM Predictive Modeling Applied in National Reporter Cleaning
Xuemei Pan; Mary Pritts, IBM; COBY LU, IBM
11:50 AM Data Science in a Hurry
Iyue Sung
12:05 PM Floor Discussion
 
 

554 * !
Wed, 8/1/2018, 2:00 PM - 3:50 PM CC-West 301
Deep Learning and Statistical Modeling with Applications — Invited Papers
Biometrics Section, Section on Statistics in Imaging, Section on Statistical Learning and Data Science, SSC
Organizer(s): Hongtu Zhu, University of Texas M.D. Anderson
Chair(s): Chuanhai Liu, Purdue University
2:05 PM Deep Learning in Quantitative Imaging Analysis
Hongtu Zhu, University of Texas M.D. Anderson
2:30 PM Cooperative Learning of Deep Energy-Based Model and Latent Variable Model via MCMC Teaching
Presentation
Ying Nian Wu, UCLA
2:55 PM Think Deeper with Deep Learning
Presentation
Saratendu Sethi, SAS Institute Inc.
3:20 PM Weight Normalized Deep Neural Networks
Presentation
Xiao Wang , Purdue University; Yixi Xu, Purdue University
3:45 PM Floor Discussion
 
 

562 *
Wed, 8/1/2018, 2:00 PM - 3:50 PM CC-West 304/305
Integrating Neuroimaging and Genomics Data — Invited Papers
Section on Statistics in Imaging, Section on Statistics in Genomics and Genetics, Section on Statistical Learning and Data Science, SSC
Organizer(s): Elizaveta Levina, University of Michigan
Chair(s): Elizaveta Levina, University of Michigan
2:05 PM Genetic Correlations Between Imaging Traits and Common Diseases
Presentation
Hongyu Zhao, Yale
2:30 PM Combining (Epi)Genetic and Imaging Data with Multivariate Data-Driven Models
Vince Calhoun, The Mind Research Network & The University of New Mexico
2:55 PM Using Omics Data to Guide Network Classification in Neuroimaging Studies of Brain Diseases
Jean Yee Hwa Yang, University of Sydney, Australia; Elizaveta Levina, University of Michigan; Mengbo Li, University of Sydney; Jesús Arroyo , University of Michigan; Daniel A. Kessler, University of Michigan
3:20 PM Discussant: Hongtu Zhu, University of North Carolina
3:45 PM Floor Discussion
 
 

566
Wed, 8/1/2018, 2:00 PM - 3:50 PM CC-West 215/216
Nonparametrics on Graphs — Invited Papers
IMS, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, SSC
Organizer(s): Ryan Tibshirani, Carnegie Mellon University
Chair(s): Edward Kennedy, Carnegie Mellon University
2:05 PM Signal Processing Over Graphs: Methods and Applications
James Sharpnack, UC Davis
2:35 PM Multiresolution Matrix Factorization for Inference on Graphs
Risi Kondor, University of Chicago
3:05 PM Fundamental Limits of Sampling Smooth Signals on Graphs
Rohan A. Varma, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University
3:35 PM Floor Discussion
 
 

571 !
Wed, 8/1/2018, 2:00 PM - 3:50 PM CC-West 115
Statistical Signal Processing Applied to Physical Activity Research — Topic Contributed Papers
Section on Statistical Computing, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics, Quality and Productivity Section
Organizer(s): Marcin Straczkiewicz, School of Public Health-Bloomington, Indiana University
Chair(s): Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health
2:05 PM A Functional Data Analysis Framework for Objectively Measured Physical Activity by Accelerometers
Presentation
Chongzhi Di, Fred Hutchinson Cancer Research Center
2:25 PM Unsupervised Clustering of Physical Activities and Its Application in Health Studies
Presentation 1 Presentation 2
Jiawei Bai, Johns Hopkins University; Ciprian Crainiceanu, Johns Hopkins University
2:45 PM Classification of Walking and Stair Climbing Based on Raw Accelerometry Data
Presentation
William Fadel, Indiana University; Jacek K Urbanek, Johns Hopkins University; Steven R Albertson, Indiana University ; Xiaochun Li, Indiana University; Andrea K Chomistek, Indiana University; Jaroslaw Harezlak, Indiana University Bloomington
3:05 PM Continuous Movelet Transformation in Application to Individual Walking Strides Segmentation in Accelerometry Data
Marta Karas, Johns Hopkins Bloomberg SPH; Jaroslaw Harezlak, Indiana University Bloomington; Marcin Straczkiewicz, School of Public Health-Bloomington, Indiana University; William Fadel, Indiana University; Ciprian Crainiceanu, Johns Hopkins University; Jacek K Urbanek, Johns Hopkins University
3:25 PM Advanced Signal Processing Methods in Walking and Body-Posture Detection in Observational Studies
Presentation
Marcin Straczkiewicz, School of Public Health-Bloomington, Indiana University; Jacek K Urbanek, Johns Hopkins University; Vadim Zipunnikov, Johns Hopkins Bloomberg School of Public Health; Nancy Glynn, University of Pittsburgh Graduate School of Public Health; Tamara Harris, National Institute on Aging; Ciprian Crainiceanu, Johns Hopkins University; Jaroslaw Harezlak, Indiana University Bloomington
3:45 PM Floor Discussion
 
 

577 * !
Wed, 8/1/2018, 2:00 PM - 3:50 PM CC-West 110
Statistical Methods for Interpreting Machine Learning Algorithms - with Implications for Targeting — Topic Contributed Papers
Section on Statistical Learning and Data Science
Organizer(s): DeDe Paul, AT&T Labs Research
Chair(s): Cheryl Flynn, AT&T Labs Research
2:05 PM On the Art and Science of Machine Learning Explanations
Patrick Hall, H20.ai
2:25 PM An Algorithm for Removing Sensitive Information
Presentation
James Johndrow, Stanford University; Kristian Lum, Human Rights Data Analysis Group
2:45 PM Local, Model-Agnostic Explanations of Machine Learning Predictions
Sameer Singh, University of California, Irvine
3:05 PM Can We Compute an Optimal Sparse Decision Tree?
Presentation
Cynthia Rudin, Duke University; Elaine Angelino, Berkeley; Nicholas Larus-Stone, Cambridge; Margo Seltzer, Harvard; Daniel Alabi, Harvard
3:25 PM Beyond Feature Attribution: Quantitative Concept-Based Interpretability with TCAV
Presentation
Been Kim, Google Brain
3:45 PM Floor Discussion
 
 

594
Wed, 8/1/2018, 2:00 PM - 3:50 PM CC-West 111
Methods for Analysis of High-Dimensional Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Bernard Lee, HedgeSPA Limited
2:05 PM The Two-To-Infinity Norm and Singular Subspace Geometry with Applications to High-Dimensional Statistics
Joshua Cape, Johns Hopkins; Dept. of Applied Math and Statistics; Minh Tang, Johns Hopkins University; Carey E Priebe, Johns Hopkins University
2:20 PM Optimal Quadratic Estimators Using Fourier Transform in the Central Subspaces
Presentation
Jiaying Weng, University of Kentucky; Xiangrong Yin, University of Kentucky
2:35 PM On Post Dimension Reduction Statistical Inference
Presentation
Kyongwon Kim, The Pennsylvania State University; Bing Li, The Pennsylvania State University
2:50 PM Kernel-Based Nonlinear Dimension Reduction for Automatic Gender Classification
Presentation
Katherine Kempfert, University of Florida; Yishi Wang, University of North Carolina Wilmington; Cuixian Chen, University of North Carolina Wilmington
3:05 PM Finding Best Low Dimensional Angles for Visualizing High-Dimensional Data
Presentation
Yanming Di, Oregon State University; Wanli Zhang, Oregon State University
3:35 PM Dimension Reduction of High-Dimensional Data Sets Based on Stepwise SVM
Presentation
Elizabeth Chou, National Chengchi University; Tzu-Wei Ko, National Chengchi University
 
 

609 * !
Thu, 8/2/2018, 8:30 AM - 10:20 AM CC-West 301
Foundation or Backdrop? - the Role of Statisticians in Academic Data Science Initiatives — Invited Panel
Section on Statistical Learning and Data Science, IMS, International Statistical Institute, SSC
Organizer(s): Tian Zheng, Columbia University
Chair(s): Tyler McCormick, University of Washington
8:35 AM Foundation or Backdrop? The Role of Statisticians in Academic Data Science Initiatives
Presentation
Panelists: Patrick J Wolfe, Purdue University
Jennifer L Hill, New York University
David Madigan, Columbia University
Edoardo M Airoldi , Harvard University
Tian Zheng, Columbia University
10:10 AM Floor Discussion
 
 

613 !
Thu, 8/2/2018, 8:30 AM - 10:20 AM CC-West 209
Recent Advances in Network Data Inference — Topic Contributed Papers
Social Statistics Section, Section on Statistical Learning and Data Science, Section on Statistical Computing, SSC
Organizer(s): Emma Jingfei Zhang, University of Miami
Chair(s): Emma Jingfei Zhang, University of Miami
8:35 AM Global Spectral Clustering in Dynamic Networks
David Choi, Carnegie Mellon University; Fuchen Liu, Carnegie Mellon University; Kathryn Roeder, Carnegie Mellon University
8:55 AM Community Detection with Covariate Information
Presentation
Yang Feng, Columbia University
9:15 AM Latent Space Approaches to Community Detection in Dynamic Networks
Presentation
Yuguo Chen, University of Illinois at Urbana-Champaign; Daniel Sewell, University of Iowa
9:35 AM Dynamic Community Detection for Multiple Networks
Sharmodeep Bhattacharyya, Oregon State University; Shirshendu Chatterjee, City University of New York
9:55 AM Post-Stratification in Network Driven Sampling
Presentation
Yilin Zhang, University of Wisconsin-Madison; Sebastien Roch, University of Wisconsin-Madison; Karl Rohe, University of Wisconsin-Madison
10:15 AM Floor Discussion
 
 

635
Thu, 8/2/2018, 8:30 AM - 10:20 AM CC-West 304/305
Advances in Machine Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Yiying Fan, Cleveland State University
8:35 AM A Comparison of Record Linkage Techniques
Presentation
Lowell Mason, U.S. Bureau of Labor Statistics
8:50 AM Assessment of Case Influence in Support Vector Machine
Presentation
Shanshan Tu, The Ohio State University; Yoonkyung Lee, Ohio State University; Yunzhang Zhu, The Ohio State University
9:20 AM Composite Local Bregman Divergences for Conditional Discrete Exponential Families
Mitsunori Ogawa, The University of Tokyo
9:35 AM Inverse Sampling for Hypothesis Testing of Multinomial Models
Hokwon Cho, University of Nevada, Las Vegas
9:50 AM An Approximation to the Information Matrix of Hidden Markov Model
Qing Ji, University of Maryland, Baltimore County; Andrew Raim, U.S. Census Bureau; Nagaraj Neerchal, University of Maryland, Baltimore County
10:05 AM Floor Discussion
 
 

642 * !
Thu, 8/2/2018, 10:30 AM - 12:20 PM CC-West 301
Data Science for Social Good — Invited Papers
Section on Statistical Learning and Data Science, Statistics and Public Policy, Social Statistics Section, Section on Statistical Computing, Survey Research Methods Section
Organizer(s): Gayle S Bieler, RTI International
Chair(s): Gayle S Bieler, RTI International
10:35 AM Data for Good: Designing for Impact
Presentation
Jake Porway, DataKind
11:00 AM Data Science + Social Science: Using Data Science to Track Arrest-Related Deaths in the US
Presentation
Duren Banks, RTI International; Peter Baumgartner, RTI International; Michael G. Planty, RTI International
11:25 AM A Model for Prioritizing Interventions for People at Risk of Incarceration
Presentation
Erika Salomon, University of Chicago
11:50 AM Discussant: Craig A. Hill, RTI International
12:15 PM Floor Discussion
 
 

645
Thu, 8/2/2018, 10:30 AM - 12:20 PM CC-West 217
Recent Developments in Score Matching with Big-Data Applications — Invited Papers
IMS, Section on Statistical Learning and Data Science, Section on Nonparametric Statistics
Organizer(s): Mladen Kolar, University of Chicago Booth School of Business
Chair(s): Mladen Kolar, University of Chicago Booth School of Business
10:35 AM The Beauty of Score Matching Estimators for Distributions on Manifolds with Some Cutting-Edge Applications
KANTI V MARDIA, UNIVERSITY OF LEEDS
11:00 AM Scoring Rules for Probabilistic Binary Classification
Presentation
Matthew Parry, University of Otago
11:25 AM Efficient and Principled Score Estimation
Presentation
Arthur Gretton, UCL
12:15 PM Floor Discussion
 
 

651 * !
Thu, 8/2/2018, 10:30 AM - 12:20 PM CC-West 203
Expanding the Tent: Undergraduate Majors in Data Science — Invited Papers
Section on Statistical Education, Section on Statistical Learning and Data Science, Section on Statistical Computing, SSC
Organizer(s): Ben Baumer, Smith College
Chair(s): Ben Baumer, Smith College
10:35 AM Dismantling Math, Stats, and CS Silos: PCMI Guidelines for Undergraduate Majors in Data Science
Presentation
Albert Y. Kim, Smith College
10:50 AM Pathways Through the Major in Statistical and Data Science at Smith
Presentation
Randi L. Garcia, Smith College
11:05 AM Herding Cats: Pros and Cons of a Large-Team Approach to Data Science at a Major Research University
David Hunter, Penn State University
11:20 AM Designing a Group Major in Data Science
Deborah Nolan, University of California, Berkeley
11:35 AM Discussant: Joseph Blitzstein, Harvard University
11:50 AM Discussant: Mine Cetinkaya-Rundel, Duke University
12:05 PM Floor Discussion
 
 

657 *
Thu, 8/2/2018, 10:30 AM - 12:20 PM CC-West 204
Statistical Network Models for Brain Connectivity Data Analysis — Topic Contributed Papers
Section on Statistics in Imaging, Section on Statistical Learning and Data Science
Organizer(s): Shuo Chen, University of Maryland, School of Medicine
Chair(s): Ming Wang, Pennsylvania State University
10:35 AM Statistical Topology of Brain Activity Networks
Presentation
Victor Solo, University of New South Wales; Ben Cassidy, Columbia University
10:55 AM Statistical Inference of Brain Connectivity Networks: a Network Topology Based Method
Presentation
Yishi Xing; Shuo Chen, University of Maryland, School of Medicine
11:15 AM Bayesian Integrative Analysis of Brain Functional Networks Incorporating Anatomical Knowledge
Suprateek Kundu, Emory University Rollins School of Public Health; Ixavier Higgins, Rollins School of Public Health-Emory University; Ying Guo, Emory University
11:35 AM Bayesian Network-On-Scalar Regression
Jian Kang, University of Michigan
11:55 AM Floor Discussion
 
 

677
Thu, 8/2/2018, 10:30 AM - 12:20 PM CC-West 111
Variable Selection Methods in Statistical Learning — Contributed Papers
Section on Statistical Learning and Data Science, SSC
Chair(s): Hyung Park, Columbia University
10:35 AM Variables and Interactions Generation for Logistic Regression Model via TreeNet and Association Rules
Pannapa Changpetch
10:50 AM Estimating the Error Variance in a High-Dimensional Linear Model
Presentation
Guo Yu, Cornell University; Jacob Bien, University of Southern California
11:05 AM Cmenet: a New Method for Bi-Level Variable Selection of Conditional Main Effects
Simon Mak, Georgia Institute of Technology; C. F. Jeff Wu, Georgia Institute of Technology
11:20 AM Subsampling for Feature Selection in Large Regression Data
Presentation
Yiying Fan, Cleveland State University
11:35 AM Semi-Supervised Learning for Joint Association and Classification Analysis of Multimodal Data
Yunfeng Zhang, Texas A&M University; Irina Gaynanova, Texas A&M Univeristy
12:05 PM Floor Discussion