Sessions Were Renumbered as of May 19.
Legend:
CC-W = McCormick Place Convention Center, West Building,
CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago,
UC = Conference Chicago at University Center
* = applied session ! = JSM meeting theme
Activity Details
20 !
Sun, 7/31/2016,
2:00 PM -
3:50 PM
CC-W175c
Ranking Methods: Infinite Permutations, Statistical Physics, Copulas, and Seriation for Discovery of Subpopulations in Big Data — Topic Contributed Papers
Section on Statistical Learning and Data Science , IMS
Organizer(s): Joseph S. Verducci, The Ohio State University
Chair(s): Stephen Bamattre, Ensemble Lending
2:05 PM
Viewing a Permutation as a Measure on the Unit Square
—
Sumit Mukherjee, Columbia University
2:25 PM
The Large N Limit of the Mallows Model
—
Shannon Starr, University of Alabama at Birmingham ; Meg L. Walters, University of Rochester
2:45 PM
A Multiparameter Mallows Model for Infinite Rankings
—
Marina Meila, University of Washington
3:05 PM
Top-K Tau-Path Subpopulation Screen for Monotone Association
—
Joseph S. Verducci, The Ohio State University ; Srinath Sampath, Hamilton Capital Management ; Adriano Caloiaro, Myatt and Johnson ; Wayne Johnson, Myatt and Johnson
3:25 PM
A Distribution Function Approach for Signal Reconstruction from Ranking Data
—
Michael Schimek, Medical University of Graz ; Vendula Svendova, Medical University of Graz
3:45 PM
Floor Discussion
36
Sun, 7/31/2016,
2:00 PM -
3:50 PM
CC-W176a
Sufficient Dimension Reduction and Projection Methods — Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Chair(s): Glen Wright Colopy, University of Oxford
2:05 PM
Selective Combinations of Central Matrices for Dimension Reduction
—
Son Nguyen
2:20 PM
On Sufficient Dimension Reduction for Functional Data
—
Jun Song, Penn State University ; Bing Li, Penn State University
2:35 PM
Determining the Number of Components in a Generalized Spiked Population Model
—
Hyo Young Choi
2:50 PM
On Likelihood Ratio Tests in Dimensionality-Restricted Models
—
Mingyue Gao ; Michael Trosset, Indiana University ; Carey Priebe, The Johns Hopkins University
3:05 PM
Partial Projective Resampling Method for Dimension Reduction: With Applications to Partially Linear Models
—
Haileab Hilafu, University of Tennessee ; Wenbo Wu, University of Oregon
3:20 PM
On Estimating Regression-Based Causal Effects Using Sufficient Dimension Reduction
—
Wei Luo, Baruch College
3:35 PM
Supervised Dimensionality Reduction for Exponential Family Data
—
Andrew Landgraf, Battelle ; Yoonkyung Lee, The Ohio State University
73
Sun, 7/31/2016,
4:00 PM -
5:50 PM
CC-W186a
Resistant and Outlier-Robust Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Carolyn Bradshaw Morgan, Hampton University
4:05 PM
Using L_1 Data Depth Unsupervised Classifier for Detecting Communities in Networks
—
Yahui Tian ; Yulia R. Gel, The University of Texas at Dallas
4:20 PM
Fast and Robust Vertex Classification by Sequential Screening
—
Li Chen, Intel Corporation ; Cencheng Shen, Temple University ; Carey Priebe, The Johns Hopkins University
4:35 PM
Efficient Robust Regression with Variable Selection via Generalized Empirical Likelihood
—
Sohini Raha, North Carolina State University ; Howard Bondell, North Carolina State University
4:50 PM
Asymptotic Relative Efficiency for Robust Estimation of the Mean of Contaminated Graphs Under a Low Rank Model
—
Runze Tang, The Johns Hopkins University ; Minh Tang, The Johns Hopkins University ; Michael Ketcha, The Johns Hopkins University ; Carey Priebe, The Johns Hopkins University ; Joshua Vogelstein, The Johns Hopkins University
5:05 PM
Fully Efficient and Outlier-Robust Estimation in the Linear Mixed Model
—
Won Gyo Suh, North Carolina State University ; Howard Bondell, North Carolina State University
5:20 PM
A Model-Selection Criterion for Regression Estimators Based on Data Depth
—
Subhabrata Majumdar, University of Minnesota - Twin Cities ; Snigdhansu Chatterjee, University of Minnesota - Twin Cities
5:35 PM
Robust Clustering Methods for Time-Evolving Brain Signals
—
Tianbo Chen, KAUST ; Ying Sun, King Abdullah University of Science and Technology ; Carolina Euan, CIMAT ; Hernando Ombao, University of California at Irvine
74
Sun, 7/31/2016,
4:00 PM -
5:50 PM
CC-W186b
Estimation and Learning in Graphical Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Brian Naughton, North Carolina State University
4:05 PM
Topological Property Hypotheses for Graphical Models
—
Junwei Lu, Princeton ; Matey Neykov, Princeton ; Han Liu, Princeton
4:20 PM
Learning Large-Scale DAG Models Using Overdispersion
—
Gunwoong Park, University of Wisconsin - Madison
4:35 PM
An Exposition on the Propriety of Restricted Boltzmann Machines
—
Andrea Kaplan, Iowa State University ; Daniel Nordman, Iowa State University ; Stephen Vardeman, Iowa State University
4:50 PM
Joint Multilevel Gaussian Graphical Model
—
Liang Shan ; Inyoung Kim, Virginia Tech
5:05 PM
A Convex Framework for High-Dimensional Sparse Cholesky Selection with Convergence Guarantees
—
Syed Rahman, University of Florida ; Kshitij Khare, University of Florida
5:20 PM
Topological Inference on Time-Varying Gaussian Graphical Models
—
Kean Ming Tan ; Junwei Lu, Princeton ; Han Liu, Princeton ; Tong Zhang, Rutgers University
5:35 PM
Blessing of Massive Scale: Spatial Graphical Model Estimation with a Total Cardinality Constraint Approach
—
Ethan Fang, Princeton ; Han Liu, Princeton ; Mendi Wang, Princeton
88
Sun, 7/31/2016,
6:00 PM -
8:00 PM
CC-Hall F1 West
The Extraordinary Power of Data — Invited Poster Presentations
Section on Statistical Learning and Data Science , Section on Statistical Graphics , Section on Statistics in Imaging , Business and Economic Statistics Section , Biometrics Section , ENAR , Section for Statistical Programmers and Analysts , Scientific and Public Affairs Advisory Committee , Section on Bayesian Statistical Science , Section on Statistics in Epidemiology , Section on Statistics in Marketing , Social Statistics Section , Statistics in Business Schools Interest Group
Chair(s): Tyler McCormick, University of Washington
1:
Communicate Better with R, R Markdown, and Shiny
—
Garrett Grolemund, RStudio
2:
Spectral Filtering for Spatial-Temporal Dynamics
—
Tian Zheng, Columbia University ; Lu Meng, Columbia University
3:
A Mixed-Effects Modeling Approach to Study the Impact of Pesticides on Farmworkers' Brain Networks Using RS-fMRI Data
—
Mohsen Bahrami, Virginia Tech ; Paul Laurienti, Wake Forest School of Medicine ; Thomas Arcury, Wake Forest School of Medicine ; Sean Simpson, Wake Forest School of Medicine
4:
Cascaded High-Dimensional Histograms: A Generative Approach to Density Estimation
—
Siong Thye Goh, MIT ; Cynthia Rudin, Duke University
5:
TV Advertising's Impact on Online Searches
—
Yonathan Schwarzkopf, Google ; Ying Liu, Google ; Makoto Uchida, Google ; Elissa Lee, Google ; Jim Koehler, Google
6:
Modeling Connectivity in High-Dimensional Time Series Data via Factor Analysis
—
Hernando Ombao, University of California at Irvine ; Yuxiao Wang, University of California at Irvine ; Chee-Ming Ting, Universiti Teknologi Malaysia
7:
Analysis of Longitudinal Multi-Sequence MRI in Multiple Sclerosis
—
Elizabeth M. Sweeney, Johns Hopkins Bloomberg School of Public Health ; Russell Shinohara, University of Pennsylvania ; John Muschelli, The Johns Hopkins University ; Daniel Reich , National Institute of Neurological Disorders and Stroke ; Ciprian Crainiceanu, The Johns Hopkins University ; Jonathan Gellar, Mathematica Policy Research ; Philip Reiss, New York University/University of Haifa ; Ani Eloyan, Brown University
8:
Law, Order, and Algorithms
—
Sharad Goel, Stanford University
9:
Defining and Estimating Reliability in Hierarchical Logistic Regression Models for Health Care Provider Profiling
—
Jessica Hwang, RAND Corporation ; John Adams, Kaiser Permanente ; Susan M. Paddock, RAND Corporation
10:
Probabilistic Cause-of-Death Assignment Using Verbal Autopsies
—
Tyler McCormick, University of Washington ; Sam Clark, University of Washington ; Zehang Li, University of Washington
11:
We Are What We Ask: Mapping the Ecosystem of Software Development Using Stack Overflow Data
—
David G. Robinson, Stack Overflow
12:
Data Science at Stitch Fix
—
Hilary Parker, Stitch Fix
13:
Text Mining on Domain Names
—
Kenneth E. Shirley, Amazon
14:
Fighting Fraud with Statistics!
—
Alyssa Frazee, Stripe
15:
Forecasting Seasonal Epidemics with Ensemble Methods and Collective Human Judgment
—
Logan Conrad Brooks, Carnegie Mellon University ; Sangwon Hyun, Carnegie Mellon University ; Ryan Tibshirani, Carnegie Mellon University
16:
Geometric Methods for Network Comparison and Multilevel Modeling
—
Anna Smith, The Ohio State University ; Catherine Calder, The Ohio State University
17:
Mixed-Effects Models for Resampled Network Statistics Improve Statistical Power to Find Differences in Functional Brain Connectivity
—
Manjari Narayan, Rice University ; Genevera Allen, Rice University
18:
Estimating the Causal Impact of Recommendation Systems from Observational Data
—
Amit Sharma, Microsoft Research ; Jake Hofman, Microsoft Research ; Duncan Watts, Microsoft Research
19:
The Future of the Journal Biostatistics
—
Dimitris Rizopoulos, Erasmus University Medical Center ; Jeffrey Leek, Johns Hopkins Bloomberg School of Public Health
20:
Sample Size Calculations for Micro-Randomized Trials in MHealth
—
Peng Liao, University of Michigan ; Ji Sun, University of Michigan ; Susan A. Murphy, University of Michigan
104 * !
Mon, 8/1/2016,
8:30 AM -
10:20 AM
CC-W196a
Advanced Machine Learning Methods for Large-Scale Heterogeneous Data — Invited Papers
Section on Statistical Learning and Data Science , Royal Statistical Society , International Chinese Statistical Association
Organizer(s): Annie Qu, University of Illinois at Urbana-Champaign
Chair(s): Annie Qu, University of Illinois at Urbana-Champaign
8:35 AM
Sparse Regression for Block Missing Data Without Imputation
—
Yufeng Liu, The University of North Carolina at Chapel Hill
9:00 AM
Automatic Summarization
—
Junhui Wang, University of Illinois at Chicago ; Xiaotong Shen, University of Minnesota ; Yiwen Sun, University of Minnesota ; Annie Qu, University of Illinois at Urbana-Champaign
9:25 AM
Query-Specific Learning to Rank via Local Smoothing
—
Junhui Wang, City University of Hong Kong
9:50 AM
Is Manifold Learning (Finding Low-Dimensional Nonlinear Embeddings for High-Dimensional Data) Impractical for Large Data?
—
Dominique Perrault-Joncas, Google ; James McQueen, University of Washington ; Marina Meila, University of Washington ; Zhongyue Zhang, University of Washington ; Jake VanderPlas, University of Washington
10:15 AM
Floor Discussion
131
Mon, 8/1/2016,
8:30 AM -
10:20 AM
CC-W195
Matrix Decomposition, Factor Models, and Applications to Recommender Systems — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Erin M. Schliep, University of Missouri
8:35 AM
Orthogonal Symmetric Non-Negative Matrix Factorization Under Stochastic Block Model
—
Subhadeep Paul, University of Illinois at Urbana-Champaign ; Yuguo Chen, University of Illinois at Urbana-Champaign
8:50 AM
Sparse Spatial Dynamic Factor Model with Basis Expansion
—
Takamitsu Araki ; Shotaro Akaho, National Institute of Advanced Industrial Science and Technology
9:05 AM
A Statistical Algorithm for Phantom Clustering Using PPCA
—
Wei Q. Deng, University of Toronto ; Radu V. Craiu, University of Toronto
9:20 AM
Learning Network Dynamics via Regularized Tensor Decomposition
—
Yun-Jhong Wu, University of Michigan ; Elizaveta Levina, University of Michigan ; Ji Zhu, University of Michigan
9:35 AM
Incorporating Informative Missingness into a Regression-Based Recommender System
—
Lin Su, North Carolina State University ; Howard Bondell, North Carolina State University
9:50 AM
E-Learning Data Analysis for Building a Personalized Recommendation System
—
Shuang Liu ; K.F. LAM, The University of Hong Kong
10:05 AM
Tree-Like Structure Classification Based on Distance Matrix LU Decomposition with Application to Galaxy Profile Data
—
Jianan Hui, University of California at Riverside ; Xinping Cui, University of California at Riverside ; James Flegal, University of California at Riverside ; Miguel Aragon-Calvo, University of California at Riverside
167 !
Mon, 8/1/2016,
10:30 AM -
12:20 PM
CC-W175c
Methodological Advances and Applications of Finite Mixture Modeling — Topic Contributed Papers
Section on Statistical Learning and Data Science , Section on Statistical Computing
Organizer(s): Semhar Michael, South Dakota State University
Chair(s): Semhar Michael, South Dakota State University
10:35 AM
Manly Transformation in Finite Mixture Modeling
—
Xuwen Zhu, University of Alabama ; Volodymyr Melnykov, University of Alabama
10:55 AM
Local Identifiability in Finite Mixture Model: A Gold Standard Solution?
—
Daeyoung Kim, University of Massachusetts - Amherst
11:15 AM
Modeling Right-Censored Loss Data Using Mixture of Distributions
—
Tatjana Miljkovic, Miami University ; Semhar Michael, South Dakota State University ; Volodymyr Melnykov, University of Alabama
11:35 AM
Model-Based Regression Clustering for High-Dimensional Data
—
Emilie Devijver
11:55 AM
Studying the Importance of Variables for Clustering
—
Volodymyr Melnykov, University of Alabama ; Yana Melnykov, University of Alabama ; Xuwen Zhu, University of Alabama
12:15 PM
Floor Discussion
176
Mon, 8/1/2016,
10:30 AM -
12:20 PM
CC-W175b
Functional Data Analysis and Extensions to Manifold Learning — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Umashanger Thayasivam, Rowan University
10:35 AM
Supervised Functional Principal Component Analysis
—
Yunlong Nie, Simon Fraser University ; Jiguo Cao, Simon Fraser University
10:50 AM
A Geometric Approach to Confidence Regions and Bands for Functional Data
—
Hyunphil Choi, Penn State University ; Matthew Reimherr, Penn State University
11:05 AM
Manifold Data Analysis
—
Hyun Bin Kang ; Matthew Reimherr, Penn State University
11:20 AM
Functional Statistical Process Control Using Elastic Methods
—
James Derek Tucker, Sandia National Laboratories
11:35 AM
Scalars-on-Function Linear Regression with Large Number of Functional Predictors
—
Ruiyan Luo ; Xin Qi, Georgia State University
11:50 AM
Nonlinear Function on Function Regression with Multiple Prediction Curves
—
Xin Qi, Georgia State University ; Ruiyan Luo
12:05 PM
Manifold Learning: Dimension Reduction Versus Parameterization Recovery
—
Michael Trosset, Indiana University ; Lijiang Guo, Indiana University
248
Mon, 8/1/2016,
2:00 PM -
3:50 PM
CC-W185a
Bayesian Methods for Shrinkage in High-Dimensional and Complex Data — Contributed Papers
Section on Statistical Learning and Data Science , International Society for Bayesian Analysis (ISBA) , Section on Bayesian Statistical Science
Chair(s): Jennifer Sniadecki, Ultragenyx
2:05 PM
A Variational Bayesian Algorithm for Variable Selection
—
Xichen Huang ; Jin Wang, University of Illinois ; Feng Liang, University of Illinois at Urbana-Champaign
2:20 PM
Bayesian Regression Using a Prior on the Model Fit
—
Brian Naughton, North Carolina State University ; Howard Bondell, North Carolina State University
2:35 PM
High-Dimensional Linear Regression via the R2-D2 Shrinkage Prior
—
Yan Zhang, North Carolina State University ; Brian J. Reich, North Carolina State University ; Howard Bondell, North Carolina State University
2:50 PM
A Topic Model for Hierarchical Documents
—
Feifei Wang ; Yang Yang, Peking University
3:05 PM
Selecting the Number of Topics in a Latent Dirichlet Allocation Topic Model
—
Dale Bowman, University of Memphis
3:20 PM
Modeling Bipartite Graph Using Dependent Indian Buffet Processes
—
Ketong Wang, University of Alabama ; Michael D. Porter, University of Alabama
3:35 PM
Adaptively Choosing Hyperparameters for Non-Local Priors in High Dimensional Bayesian Variable Selection
—
Amir Nikooienejad, Texas A&M University ; Valen E. Johnson, Texas A&M University
249
Mon, 8/1/2016,
2:00 PM -
3:50 PM
CC-W186a
Regularization and Prediction in Time Series and Longitudinal Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Chuck Kincaid, Experis
2:05 PM
Joint Modeling of Correlated Binary Response and Longitudinal Covariates via Random Forest Applied to Glaucoma Progression Prediction
—
Juanjuan Fan, San Diego State University ; Lucie Sharpsten, United Health Group ; Xiaogang Su, The University of Texas at El Paso ; Shaban Demirel, Devers Eye Institute ; Richard Levine, San Diego State University
2:20 PM
Data Representation and Pattern Recognition in Financial Time Series
—
Seunghye Jung Wilson, George Mason University ; James E. Gentle, George Mason University
2:35 PM
Estimation of Multi-Granger Network Causal Models
—
Andrey Skripnikov, University of Florida ; George Michailidis, University of Florida
2:50 PM
High-Dimensional Regularized Estimation in Time Series Under Mixing Conditions
—
Kam Chung Wong, University of Michigan ; Ambuj Tewari, University of Michigan ; Zifan Li, University of Michigan
3:05 PM
Next-Generation Flow Field Forecasting: Organizing and Searching Historical Time Series Data Using CART
—
Kyle Caudle, South Dakota School of Mines and Technology ; Michael Frey, Bucknell University ; Patrick Fleming, South Dakota School of Mines and Technology
3:20 PM
Longitudinal Network Prediction with Applications to Network-Based Interventions
—
Ravi Goyal, Mathematica Policy Research
3:35 PM
Applications of Machine Learning in Environmetrics: Detecting Dynamic Trend-Based Clusters
—
Xin Huang, The University of Texas at Dallas ; Iliyan R. Iliev, The University of Texas at Dallas ; Lyubchich Vyacheslav, University of Maryland Center for Environmental Science ; Alexander Brenning , University of Jena ; Yulia R. Gel, The University of Texas at Dallas
298 * !
Tue, 8/2/2016,
8:30 AM -
10:20 AM
CC-W187c
Regularization: A Versatile Technique for High-Dimensional Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science , Korean International Statistical Society
Organizer(s): Cheolwoo Park, University of Georgia
Chair(s): Seunggeun Lee, University of Michigan
8:35 AM
Regularized LDA for High-Dimensional Data
—
Jeongyoun Ahn, University of Georgia ; Yongho Jeon, Yonsei University
8:55 AM
Variable Selection for Haitian Tuberculosis (TB) Patients Metabolomics Studies
—
Myung Hee Lee, Weill Cornell Medicine
9:15 AM
Principal Quantile Regression for Sufficient Dimension Reduction with Heteroscedasticity
—
Chong Wang, North Carolina State University ; Yichao Wu, North Carolina State University ; Seeing Jun Shin, Korea University
9:35 AM
Sparse Additive Graphical Models
—
Hyonho Chun, Purdue University ; Ji Hwan Oh, Purdue University
9:55 AM
Discussant: Woncheol Jang, Seoul National University
10:15 AM
Floor Discussion
314
Tue, 8/2/2016,
8:30 AM -
10:20 AM
CC-W187a
Regularization Methods for Sparsity and Smoothness — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jie Yang, University of Illinois at Chicago
8:35 AM
Scaled Concave Penalized Regression
—
Long Feng ; Cun-Hui Zhang, Rutgers University
8:50 AM
Hierarchical Sparse Modeling: A Choice of Two Regularizers
—
Xiaohan Yan, Cornell University ; Jacob Bien, Cornell University
9:05 AM
Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High-Dimensional Mediators
—
Yi Zhao, Brown University ; Xi Luo, Brown University
9:20 AM
Stagewise Generalized Estimating Equations
—
Gregory Vaughan, University of Connecticut ; Robert Aseltine, University of Connecticut Health Center ; Kun Chen, University of Connecticut ; Jun Yan, University of Connecticut
9:35 AM
CoCoLasso for High-Dimensional Error-in-Variables Regression
—
Abhirup Datta, University of Minnesota ; Hui Zou, University of Minnesota
9:50 AM
Risk Estimation for High-Dimensional Lasso Regression
—
Daniel McDonald, Indiana University ; Darren Homrighausen, Colorado State University
10:05 AM
Nonparametric Regression with Adaptive Smoothness via a Convex Hierarchical Penalty
—
Asad Haris, University of Washington ; Ali Shojaie, University of Washington ; Noah Simon, University of Washington
315
Tue, 8/2/2016,
8:30 AM -
10:20 AM
CC-W186b
Complex and Multiscale Network Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Yan Zhang, North Carolina State University
8:35 AM
Hypothesis Testing for Detecting Changes Within a Barabási-Albert Network
—
Fairul Mohd-Zaid, Air Force Research Lab ; Christine Schubert Kabban, Air Force Institute of Technology ; Edward White, Air Force Institute of Technology ; Richard Deckro, Air Force Institute of Technology
8:50 AM
Multiscale Network Analysis Using an Adaptive Haar-Like Transformation
—
Xinyu Kang, Boston University ; Piotr Fryzlewicz, London School of Economics ; Eric D. Kolaczyk, Boston University
9:05 AM
Network Degree Distribution Inference Under Sampling
—
Aleksandrina Goeva, Boston University ; Richard Lehoucq, Sandia National Laboratories ; Eric D. Kolaczyk, Boston University
9:20 AM
A Blockmodel for Node Popularity in Networks with Community Structure
—
Srijan Sengupta, University of Illinois at Urbana-Champaign ; Yuguo Chen, University of Illinois at Urbana-Champaign
9:35 AM
A Point Process Model with Latent Positions for Network Modeling
—
Bomin Kim
9:50 AM
Network Cross-Validation by Edge Sampling
—
Tianxi Li, University of Michigan ; Elizaveta Levina, University of Michigan ; Ji Zhu, University of Michigan
10:05 AM
Uncertainty Assessment for Source Estimation of Spreading Processes on Complex Networks
—
Juliane Manitz, Boston University ; Jun Li, Boston University ; Eric D. Kolaczyk, Boston University
344 * !
Tue, 8/2/2016,
10:30 AM -
12:20 PM
CC-W183c
SLDS 2016 Student Paper Awards Session — Topic Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Organizer(s): Tian Zheng, Columbia University
Chair(s): Tian Zheng, Columbia University
10:35 AM
A Group-Specific Recommender System
—
Xuan Bi ; Annie Qu, University of Illinois at Urbana-Champaign ; Junhui Wang, City University of Hong Kong ; Xiaotong Shen, University of Minnesota
10:55 AM
Scalable Bayesian Rule Lists
—
Hongyu Yang, MIT EECS ; Cynthia Rudin, Duke University ; Margo Seltzer, Harvard
11:15 AM
Model-Based Clustering for Large-Scale Dynamic Networks
—
Kevin Lee, Penn State University ; Lingzhou Xue, Penn State University ; David Hunter, Penn State University
11:35 AM
Sparse Multidimensional Graphical Models: A Unified Bayesian Framework
—
Yang Ni ; Francesco Stingo, MD Anderson Cancer Center ; Veera Baladandayuthapani, MD Anderson Cancer Center
11:55 AM
Another Look at Distance-Weighted Discrimination
—
Boxiang Wang, University of Minnesota ; Hui Zou, University of Minnesota
12:15 PM
Floor Discussion
353
Tue, 8/2/2016,
10:30 AM -
12:20 PM
CC-W181a
SPEED: Statistical Learning and Data Science — Contributed Speed
Section on Statistical Learning and Data Science
Chair(s): Michael Weylandt, Rice University
10:35 AM
A Random Forest of Modified Interaction Trees for Treatment Decision Rules
—
Zhen Zeng, Merck ; Zheng Wei, Sanofi US ; Yuefeng Lu, Sanofi US
10:40 AM
Modern Projection Pursuit Ellipse for High-Dimensional Data
—
Jang Ik Cho, Case Western Reserve University ; Xiaoyan Wei, Case Western Reserve University ; Jiayang Sun, Case Western Reserve University
10:45 AM
Model-Free Estimation of Task-Based Dynamic Functional Connectivity and Its Confidence Intervals
—
Maria Kudela, Indiana University ; Mario Dzemidzic, Indiana University School of Medicine ; Brandon G. Oberlin, Indiana University School of Medicine ; Joaquín Goñi, Purdue University ; David A. Kareken, Indiana University School of Medicine ; Jaroslaw Harezlak, Indiana University Fairbanks School of Public Health
10:50 AM
Topological Statistical Inference for Location Parameters via Frechet Functions
—
Ruite Guo, Florida State University
10:55 AM
An R Package Enabling Likelihood-Based Inference for Generalized Linear Mixed Models
—
Christina Knudson
11:00 AM
Community Extraction for Networks with Node Covariates via Pseudo-Likelihood Method
—
Chengan Du, George Mason University ; Qing Pan, The George Washington University ; Yunpeng Zhao, George Mason University
11:05 AM
Predicting Job Application Success with Two-Stage, Bayesian Modeling of Features Extracted from Candidate-Role Pairs
—
Jon Krohn, untapt ; Gabe Rives-Corbett, untapt ; Ed Donner, untapt
11:10 AM
Likelihood Methods for Non-Negative Matrix Factorization
—
Frank Shen, Penn State University
11:15 AM
Models for Understanding and Predicting Consumer Perception of Radiance
—
Supriya A. K. Satwah, Unilever ; Anthony Cece, Unilever ; Robert Velthuizen, Unilever
11:30 AM
High-Dimensional Inference for Partial Linear Models
—
Zhuqing Yu
11:35 AM
Penalized Principal Logistic Regression for Sparse Sufficient Dimension Reduction
—
Seung Jun Shin, Korea University ; Andreas Artemiou, Cardiff University
11:40 AM
Dimension-Reduction Techniques for Predictive Modeling
—
Zhen Zhang, C Spire ; Lei Zhang, Mississippi State Department of Health ; Kendell Churchwell, C Spire ; James Veillette, C Spire
11:50 AM
The Knockoff Filter for FDR Control in Group-Sparse and Multitask Regression
—
Ran Dai, The University of Chicago ; Rina Foygel Barber, The University of Chicago
11:55 AM
Maximizing Text Mining Performance: The Impact of Pre-Processing
—
Dario Gregori, University of Padova ; Paola Berchialla, University of Torino ; Nicola Soriani, University of Padova ; Ileana Baldi, University of Padova ; Corrado Lanera, University of Padova
361
Tue, 8/2/2016,
10:30 AM -
12:20 PM
CC-W186b
Applications of Ensemble and Tree-Based Methods — Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Chair(s): Dirk Moore, Rutgers School of Public Health
10:35 AM
Decoding Brain States from fMRI Data with a Machine Learning Method
—
Elizabeth Chou
10:50 AM
Classification and Regression Tree Modeling of Correlated Binary Outcomes
—
Jaime Speiser, Medical University of South Carolina ; Valerie Durkalski-Mauldin, Medical University of South Carolina ; Dongjun Chung, Medical University of South Carolina ; Bethany Wolf, Medical University of South Carolina
11:05 AM
Ranking Homologous Proteins Using an Ensemble of Logistic Regression Models Based on Subsets of Feature Variables
—
Jabed Tomal, University of Toronto ; William J. Welch, The University of British Columbia ; Ruben H. Zamar, The University of British Columbia
11:20 AM
An Approach to the Multivariate Two-Sample Problem Using Classification and Regression Trees and Minimum-Weight Spanning Subgraphs
—
David Ruth ; Samuel Buttrey, Naval Postgraduate School ; Lyn Whitaker, Naval Postgraduate School
11:35 AM
Methodological Strategies to Define a Generalizable Model for Machine Learning Ensemble Techniques
—
Joel Correa da Rosa, Rockefeller University ; Lewis Tomalin, Icahn School of Medicine at Mount Sinai ; Mayte Suárez-Fariñas, Icahn School of Medicine at Mount Sinai
11:50 AM
Selecting Decision Rules from Tree Ensembles
—
Damir Spisic, IBM ; Jing Xu, IBM
12:05 PM
Floor Discussion
406 * !
Tue, 8/2/2016,
2:00 PM -
3:50 PM
CC-W180
Recent Advances in High-Dimensional Statistics and Computational Methods — Invited Papers
Section on Statistical Learning and Data Science , Section on Statistics in Imaging
Organizer(s): Dan Yang, Rutgers University
Chair(s): Po-Ling Loh, University of Pennsylvania
2:05 PM
Oracle Inequalities for Network Models and Sparse Graphon Estimation
—
Alexandre Tsybakov, ENSAE ; Olga Klopp, University Paris 10/CREST ; Nicolas Verzelen, INRA
2:25 PM
Bilinear Regression with Matrix Covariates in High Dimensions
—
Dan Yang, Rutgers University ; Dong Wang ; Hongtu Zhu, The University of North Carolina at Chapel Hill ; Haipeng Shen, The University of Hong Kong
2:45 PM
Convex Regularization for High-Dimensional Tensor Regression
—
Ming Yuan, University of Wisconsin ; Garvesh Raskutti, University of Wisconsin - Madison
3:05 PM
Why Do Statisticians Treat Predictors as Fixed? A Conspiracy Theory
—
Andreas Buja, The Wharton School ; Richard Berk, The Wharton School ; Lawrence D. Brown, University of Pennsylvania ; Edward I. George, The Wharton School ; Emil Pitkin, The Wharton School ; Mikhail Traskin, Amazon.com ; Linda Zhao, The Wharton School ; Kai Zhang, The University of North Carolina at Chapel Hill
3:25 PM
Sum of Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems
—
Yash Deshpande, Stanford University
3:45 PM
Floor Discussion
432
Tue, 8/2/2016,
2:00 PM -
3:50 PM
CC-W178b
New Approaches in Classification Methods — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jason Gillikin, Priority Health
2:05 PM
L-CC Classification and Variable Selection for Multi-Label Data Sets
—
Monika Stupalova du Toit, Stellenbosch University ; Sarel Steel, Stellenbosch University
2:20 PM
Multi-Class ROC Tree and Random Forest for Imbalanced Data Classification
—
Jiaju Yan, SUNY Stony Brook ; Wei Zhu, SUNY Stony Brook ; Bowen Song, Ocean University of China
2:35 PM
Efficient Sampling Strategy for SVM Through Semi-Supervised Active Learning
—
Yaru Shi, University of Illinois at Chicago ; Yoonsang Kim, University of Illinois at Chicago ; Ganna Kostygina, University of Illinois at Chicago ; Sherry Emery, University of Illinois at Chicago
2:50 PM
Feature Selection for Class-Imbalanced Data Using Binormal Precision-Recall Curves
—
Zhongkai Liu, North Carolina State University ; Howard Bondell, North Carolina State University
3:05 PM
Nonparametric Classification Using a Forest Dependency Structure
—
Mary Frances Dorn, Texas A&M University ; Clifford Spiegelman, Texas A&M University ; Amit Moscovich, Weizmann Institute of Science ; Boaz Nadler, Weizmann Institute of Science
3:20 PM
Statistical Learning Toolbox for Prediction
—
Umashanger Thayasivam, Rowan University
3:35 PM
Big Data Methods for Scraping Government Tax Revenue from the Web
—
Brian Dumbacher, U.S. Census Bureau ; Cavan Capps, U.S. Census Bureau
433
Tue, 8/2/2016,
2:00 PM -
3:50 PM
CC-W182
Random Graph and Network Models — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Barbara J. Robles, Federal Reserve Board
2:05 PM
Exponential Random Graph Models with Stable (Nonparametric) Statistics
—
Goeran Kauermann, Ludwig-Maximilians-University Munich
2:20 PM
Generalized Exponential Random Graph Models: Statistical Inference for Weighted Graphs
—
James D. Wilson, University of San Francisco ; Shankar Bhamidi, The University of North Carolina at Chapel Hill
2:35 PM
Reconstruction of Directed Acyclic Graphs Networks Based on Prior Causal Ordering Information with Applications to Gene Regulatory Networks
—
Pei-Li Wang, University of Florida ; George Michailidis, University of Florida
2:50 PM
Link Prediction via Matrix Decomposition by Solving Lyapunov Equations
—
Yunpeng Zhao, George Mason University
3:05 PM
Risk, Value, and Popularity: A Network-Based Approach to Stock Portfolio Diversification
—
Natallia Katenka, University of Rhode Island ; Gregory Breard, University of Rhode Island
3:20 PM
Lasso-Type Network Community Detection Within Latent Space
—
Shiwen Shen ; Edsel Aldea Pena, University of South Carolina
3:35 PM
Floor Discussion
445
Tue, 8/2/2016,
2:00 PM -
3:50 PM
CC-Hall F1 West
Contributed Poster Presentations: Section on Statistical Learning and Data Science — Contributed Poster Presentations
Section on Statistical Learning and Data Science , Section on Statistics in Imaging , International Chinese Statistical Association
Chair(s): Genevera Allen, Rice University
29:
Angle-Based Distance-Weighted Support Vector Machine in Multicategory Classification
—
Hui Sun, Purdue University ; Bruce A. Craig, Purdue University ; Lingsong Zhang, Purdue University
30:
A New Distribution to Describe Big Data
—
Yuanyuan Zhang, University of Manchester
32:
Identification of Solids in Hyperspectral Images Using Spectral Features from Gaussian Basis Functions
—
Cory Lanker, Lawrence Livermore National Laboratory ; Milton O. Smith, Lawrence Livermore National Laboratory
33:
ZIP Codes and Neural Networks: Machine Learning for Handwritten Number Recognition
—
Cuixian Chen, The University of North Carolina at Wilmington ; Taylor Harbold, The University of North Carolina at Wilmington ; Courtney Rasmussen, The University of North Carolina at Wilmington ; Michelle Page, The University of North Carolina at Wilmington
34:
Constrained Canonical Covariance Analysis by Using Tucker2 Model
—
Jun Tsuchida, Doshisha University ; Hiroshi Yadohisa, Doshisha University
35:
Sparse Predictive Modeling for Bank Telemarketing Success Using Smooth-Threshold Estimating Equations
—
Yoshinori Kawasaki, Institute of Statistical Mathematics ; Masao Ueki, Kurume University
37:
Predicting Binary Outcome with Unequal Misclassification Cost
—
Shuchismita Sarkar, University of Alabama ; Michael D. Porter, University of Alabama
39:
Empirical Evaluation of Bayesian Network Classifiers
—
Weihua Shi, SAS Institute
40:
Employing Machine Learning Approaches in Social Scientific Analyses
—
Arne Bethmann, Institute for Employment Research ; Jonas Beste, Institute for Employment Research
41:
A Multilocus Genetic Score for Physical Activity
—
Lingyao Yang, Stanford University ; Haley Hedlin, Stanford University
42:
Anomaly Detection in Time Series of Dependent Stochastic Block Model Graphs
—
Heng Wang, Machine Zone ; Albert Liu, Ward Melville High School ; Youngser Park, The Johns Hopkins University ; Carey Priebe, The Johns Hopkins University
43:
Clustering for Personalized Preference Prediction
—
Fan Yang, University of Minnesota - Twin Cities ; Xiaotong Shen, University of Minnesota
46:
Divide and Recombine (DandR) with Tessera: High-Performance Computing for the Analysis of Big Data and High-Complexity Analytics
—
Yuying Song, Purdue University ; Bowei Xi, Purdue University ; Ryan Hafen, Hafen Consulting ; William S. Cleveland, Purdue University
48:
Understanding Grand Strategy: Text and Topic Analysis of Presidential Speeches
—
Reagan Rose
49:
AMON: An Open Source Architecture for Online Monitoring, Statistical Analysis, and Forensics of Multi-Gigabit Streams
—
Shrijita Bhattacharya, University of Michigan ; Michael Khallitsis, Merit
50:
Estimating Coefficients of Direction in Single Index Model for Large $P$ Small $N$ Problem
—
Jin Xie, University of Kentucky ; Xiangrong Yin, University of Kentucky
51:
C.Logic: An Algorithm to Classify Dichotomous Disease Outcomes Using Interactions Between Dichotomous and Continuous Predictors
—
Sybil Prince Nelson
52:
Hypothesis Testing and Prediction of the Self-Triggering Cox Model for Recurrent Event Data
—
Jung In Kim ; Jason Fine, The University of North Carolina at Chapel Hill ; Feng-Chang Lin, The University of North Carolina at Chapel Hill
53:
Regret Bounds for a Thompson Sampling Algorithm with Application to Emerging Infectious Disease
—
Tao Hu, North Carolina State University ; Eric Laber, North Carolina State University
54:
Group Discrimination Using Sparse Network Modeling of Resting-State fMRI
—
Maria Puhl, University of Tulsa ; William Coberly, University of Tulsa ; Alejandro Hernandez, University of Tulsa ; Kyle Simmons, Laureate Institute for Brain Research
449
Tue, 8/2/2016,
2:00 PM -
2:45 PM
CC-Hall F1 West
SPEED: Statistical Learning and Data Science, Part 2A — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Genevera Allen, Rice University
11:
A Random Forest of Modified Interaction Trees for Treatment Decision Rules
—
Zhen Zeng, Merck ; Zheng Wei, Sanofi US ; Yuefeng Lu, Sanofi US
12:
Modern Projection Pursuit Ellipse for High-Dimensional Data
—
Jang Ik Cho, Case Western Reserve University ; Xiaoyan Wei, Case Western Reserve University ; Jiayang Sun, Case Western Reserve University
13:
Model-Free Estimation of Task-Based Dynamic Functional Connectivity and Its Confidence Intervals
—
Maria Kudela, Indiana University ; Mario Dzemidzic, Indiana University School of Medicine ; Brandon G. Oberlin, Indiana University School of Medicine ; Joaquín Goñi, Purdue University ; David A. Kareken, Indiana University School of Medicine ; Jaroslaw Harezlak, Indiana University Fairbanks School of Public Health
14:
Topological Statistical Inference for Location Parameters via Frechet Functions
—
Ruite Guo, Florida State University
15:
An R Package Enabling Likelihood-Based Inference for Generalized Linear Mixed Models
—
Christina Knudson
16:
Community Extraction for Networks with Node Covariates via Pseudo-Likelihood Method
—
Chengan Du, George Mason University ; Qing Pan, The George Washington University ; Yunpeng Zhao, George Mason University
17:
Predicting Job Application Success with Two-Stage, Bayesian Modeling of Features Extracted from Candidate-Role Pairs
—
Jon Krohn, untapt ; Gabe Rives-Corbett, untapt ; Ed Donner, untapt
18:
Likelihood Methods for Non-Negative Matrix Factorization
—
Frank Shen, Penn State University
20:
Models for Understanding and Predicting Consumer Perception of Radiance
—
Supriya A. K. Satwah, Unilever ; Anthony Cece, Unilever ; Robert Velthuizen, Unilever
The oral portion will take place during Session 213151
452
Tue, 8/2/2016,
3:05 PM -
3:50 PM
CC-Hall F1 West
SPEED: Statistical Learning and Data Science, Part 2B — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Genevera Allen, Rice University
11:
High-Dimensional Inference for Partial Linear Models
—
Zhuqing Yu
12:
Penalized Principal Logistic Regression for Sparse Sufficient Dimension Reduction
—
Seung Jun Shin, Korea University ; Andreas Artemiou, Cardiff University
13:
Dimension-Reduction Techniques for Predictive Modeling
—
Zhen Zhang, C Spire ; Lei Zhang, Mississippi State Department of Health ; Kendell Churchwell, C Spire ; James Veillette, C Spire
16:
The Knockoff Filter for FDR Control in Group-Sparse and Multitask Regression
—
Ran Dai, The University of Chicago ; Rina Foygel Barber, The University of Chicago
17:
Maximizing Text Mining Performance: The Impact of Pre-Processing
—
Dario Gregori, University of Padova ; Paola Berchialla, University of Torino ; Nicola Soriani, University of Padova ; Ileana Baldi, University of Padova ; Corrado Lanera, University of Padova
The oral portion will take place during Session 213151
485 * !
Wed, 8/3/2016,
8:30 AM -
10:20 AM
CC-W194b
Recent Advances in Functional Data Analysis — Topic Contributed Papers
Section on Statistical Learning and Data Science , International Chinese Statistical Association
Organizer(s): Xiaoke Zhang, University of Delaware
Chair(s): Raymond Wong, Iowa State University
8:35 AM
Single-Index Models for Function-on-Function Regression
—
Guanqun Cao, Auburn University ; Li Wang, Iowa State University
8:55 AM
Weighing Schemes for Functional Data
—
Xiaoke Zhang, University of Delaware ; Jane-Ling Wang, University of California at Davis
9:15 AM
Historical Functional Cox Regression, with an Application to Prediction of Multiple Sclerosis Lesions
—
Philip Reiss, New York University ; Elizabeth M. Sweeney, Johns Hopkins Bloomberg School of Public Health ; Jonathan Gellar, Mathematica Policy Research
9:35 AM
Nonparametric Estimation of Stationary Covariance Functions
—
Li Wang, Iowa State University ; Jiangyan Wang, Soochow University ; Guanqun Cao, Auburn University
9:55 AM
Functional Data Methods for Child Growth Data
—
Luo Xiao, North Carolina State University ; Andrew Leroux, Johns Hopkins Bloomberg School of Public Health ; William Checkley, The Johns Hopkins University ; Ciprian Crainiceanu, The Johns Hopkins University
10:15 AM
Floor Discussion
498
Wed, 8/3/2016,
8:30 AM -
10:20 AM
CC-W194a
Community, Social, and Anomaly Detection in Networks — Contributed Papers
Section on Statistical Learning and Data Science , Royal Statistical Society
Chair(s): Georgiy Bobashev, RTI International
8:35 AM
Self-Similarity Estimation for Cyber-Security
—
Marina Evangelou, Imperial College London ; Niall Adams, Imperial College London
8:50 AM
Analysis of Community Evolution in Networks
—
Giuliana Pallotta, Lawrence Livermore National Laboratory ; Goran Konjevod, Lawrence Livermore National Laboratory
9:05 AM
Anomaly Detection in Time-Evolving Networks Using Tensor Spectrum
—
Ruikai Cao, The University of Texas at Dallas ; Yulia R. Gel, The University of Texas at Dallas
9:20 AM
Structural Balance in Village Social Networks with Antagonistic Ties
—
Derek Feng, Yale University
9:35 AM
Learning the Underlying Social Network from Continuous-Time Pairwise Interaction Data
—
Wesley Lee, University of Washington ; Bailey Fosdick, Colorado State University ; Tyler McCormick, University of Washington
9:50 AM
An ROC Approach to Estimating Interpersonal Networks
—
Deniz Yenigun, Istanbul Bilgi University ; Gunes Ertan, Koc University ; Michael Siciliano, University of Illinois at Chicago
10:05 AM
Floor Discussion
499
Wed, 8/3/2016,
8:30 AM -
10:20 AM
CC-W193b
Statistical Learning Approaches to Biological Inference Problems — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Nusrat Jahan, James Madison University
8:35 AM
Incorporating Biological Information in Sparse Principal Component Analysis with Application to Genomic Data
—
Ziyi Li, Emory University ; Qi Long, Emory University ; Sandra Safo, Emory University
8:50 AM
Detecting Real-Time Substance Use from Wearable Biosensor Data Stream
—
Chanpaul Jin Wang, University of Massachusetts Medical School ; Hua Fang, University of Massachusetts Medical School ; Stephanie Carreiro, University of Massachusetts Medical School ; Honggang Wang, University of Massachusetts - Dartmouth ; Edward Boyer, University of Massachusetts Medical School
9:05 AM
Statistics and Machine Learning in Pharmacovigilance for Signal Detection of Cardiovascular Risks
—
James Chen, FDA/NCTR ; Weizhong Zhao , FDA/NCTR ; Wen Zou, FDA/NCTR
9:20 AM
Deep Spatial Learning for Forensic Geolocation with Microbiome Data
—
Neal Grantham ; Brian J. Reich, North Carolina State University ; Eric Laber, North Carolina State University
9:35 AM
Human Detection from Images with Supervised Kernel PCA
—
Yishi Wang, The University of North Carolina at Wilmington ; Troy Kling, University of Florida
9:50 AM
Genome-Wide Association Studies Using a Penalized Moving-Window Regression
—
Minli Bao, University of Iowa ; Kai Wang, University of Iowa
10:05 AM
Data Normalization by Fisher-Yates Transformation
—
Yayan Zhang, Merck ; Javier Cabrera, Rutgers University ; Birol Emir, Pfizer Inc & Columbia University
518 !
Wed, 8/3/2016,
10:30 AM -
12:20 PM
CC-W179a
Getting High on Statistics: Powering Large-Scale Data Analysis — Invited Papers
Section on Statistical Learning and Data Science
Organizer(s): Ananda Sen, University of Michigan
Chair(s): Ananda Sen, University of Michigan
10:35 AM
Mixed Graphical Model Selection with Applications to Integrative Genomics
—
Genevera Allen, Rice University ; Yulia Baker, Rice University
11:05 AM
A Hypothesis-Testing Framework for Modularity-Based Network Community Detection
—
Jingfei Zhang, University of Miami ; Yuguo Chen, University of Illinois at Urbana-Champaign
11:35 AM
Iterative Random Forests: Stable Identification of High-Order Interactions in Heterogeneous and High-Dimensional Data
—
Sumanta Basu, University of California at Berkeley ; Bin Yu, University of California at Berkeley
12:05 PM
Floor Discussion
544
Wed, 8/3/2016,
10:30 AM -
12:20 PM
CC-W178b
Statistical Learning with Censored Data and Systematic Sampling — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jonathan Hobbs, Jet Propulsion Laboratory
10:35 AM
Modeling an Ordinal Outcome in High Dimensions with Nonparametric Feature Augmentation and Proportional Odds Boosting
—
Kyle Ferber, Virginia Commonwealth University ; Kellie J. Archer, Virginia Commonwealth University
10:50 AM
An approximate L0-based variable selection method for high dimensional data
—
Zhihua Sun, Ocean University of China ; Gang Li, University of California at Los Angeles
11:05 AM
Selection for Semiparametric Odds Ratio Model via Adaptive Screening
—
Jinsong Chen, University of Illinois at Chicago ; Huayun Chen, University of Illinois at Chicago
11:20 AM
Using Inverse Probability of Censoring Weighted Bagging to Adapt Machine-Learning Techniques to Censored Data
—
Ales Kotalik, University of Minnesota ; Julian Wolfson, University of Minnesota ; David Vock, University of Minnesota School of Public Health ; Gediminas Adomavicius, University of Minnesota ; Sunayan Bandyopadhyay, University of Minnesota
11:35 AM
Bayesian Neural Network for Predicting Survival Time of Competing Risks
—
Taysseer Sharaf, Slippery Rock University
11:50 AM
Systematic Sampling Design with Application to Data Splitting
—
Redouane Betrouni, George Mason University ; James E. Gentle, George Mason University
12:05 PM
Statistical Computation with Mixture Data
—
Shiju Zhang, St. Cloud State University
608
Wed, 8/3/2016,
2:00 PM -
3:50 PM
CC-W181a
Variable Selection Methods for Sparse Learning from Data — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Bethany Wolf, Medical University of South Carolina
2:05 PM
Group Feature Selection in Ultrahigh-Dimensional Generalized Varying-Coefficient Linear Models
—
Songshan Yang ; Runze Li, Penn State University
2:20 PM
Testing-Based Variable Selection for High-Dimensional Linear Models
—
Siliang Gong, The University of North Carolina at Chapel Hill ; Kai Zhang, The University of North Carolina at Chapel Hill ; Yufeng Liu, The University of North Carolina at Chapel Hill
2:35 PM
Subsampling for Feature Selection from Large Regression Data
—
Yiying Fan, Cleveland State University ; Jiayang Sun, Case Western Reserve University
2:50 PM
Flexible Modeling of Local Dependence in Variables with a Natural Ordering
—
Guo Yu, Cornell University ; Jacob Bien, Cornell University
3:05 PM
Sparsity-Oriented Importance Learning
—
Chenglong Ye, University of Minnesota ; Yi Yang, McGill University ; Yuhong Yang, University of Minnesota
3:20 PM
ThrEEBoost: Thresholded Boosting for Variable Selection and Prediction via Estimating Equations
—
Benjamin Brown
3:35 PM
Floor Discussion
609
Wed, 8/3/2016,
2:00 PM -
3:50 PM
CC-W181b
Spatio-Temporal Models, Prediction, and Anomaly Detection — Contributed Papers
Section on Statistical Learning and Data Science , Royal Statistical Society
Chair(s): Sarah Kalicin, Intel Corporation
2:05 PM
Point Process Modeling with Spatiotemporal Covariates for Predicting Crime
—
Alex Reinhart, Carnegie Mellon University ; Xizhen Cai, Carnegie Mellon University ; Joel Greenhouse, Carnegie Mellon University
2:20 PM
Identification of Homogeneous Areas Through Lattice-Based Spatio-Temporal Clustering
—
Rodrigue Ngueyep Tzoumpe, IBM Research ; Huijing Jiang, IBM ; YoungDeok Hwang, IBM T. J. Watson Research Center
2:35 PM
Generalized Difference in Difference Models with Gaussian Processes
—
William Herlands, Carnegie Mellon University ; Daniel B. Neill, Carnegie Mellon University ; Akshaya Jha, Carnegie Mellon University ; Seth Flaxman, University of Oxford ; Kun Zhang, Carnegie Mellon University
2:50 PM
Identifying Typical Patterns and Atypical Behavior in Copious Amounts of Streaming Data
—
Brett Amidan, Pacific Northwest National Laboratory ; James Follum, Pacific Northwest National Laboratory
3:05 PM
Archetypal Analysis: Three Case Studies
—
Anna Quach ; Adele Cutler, Utah State University
3:20 PM
Vertex Nomination via Seeded Graph Matching
—
Heather Patsolic, The Johns Hopkins University ; Vince Lyzinski, The Johns Hopkins University ; Carey Priebe, The Johns Hopkins University
3:35 PM
Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
—
Edward McFowland, Carlson School of Management ; Sriram Somanchi, University of Notre Dame ; Daniel B. Neill, Carnegie Mellon University
641 * !
Thu, 8/4/2016,
8:30 AM -
10:20 AM
CC-W180
Advancing Precision Medicine Using Innovative Subgroup Identification Methods — Topic Contributed Papers
Section on Statistical Learning and Data Science , Biopharmaceutical Section , International Chinese Statistical Association
Organizer(s): Qi Tang, AbbVie
Chair(s): Richard A. Rode, AbbVie
8:35 AM
Use of the VG (Virtual Twins Combined with GUIDE) Method in the Development of Precision Medicines
—
Jia Jia ; Qi Tang, AbbVie ; Wangang Xie, AbbVie ; Richard A. Rode, AbbVie
8:55 AM
Comparison of Some Subgroup Identification Algorithms for Precision Medicine in Drug Development
—
Xin Huang ; Yan Sun, AbbVie ; Saptarshi Chatterjee, AbbVie ; Viswanath Devanarayan, AbbVie
9:15 AM
Subgroup Identification Based on Multiple Outcomes
—
Chensheng Kuang, University of Wisconsin - Madison ; Menggang Yu, University of Wisconsin - Madison ; Sijian Wang, University of Wisconsin - Madison
9:35 AM
Development of Predictive Signature to Identify Patient Subgroups with Differential Treatment Effect
—
Yu-Chuan Chen ; James Chen, FDA/NCTR ; Un Jung Lee, FDA/NCTR
9:55 AM
Subgroup Identification in a Learn-and-Confirm Setting
—
Lei Shen, Eli Lilly and Company
10:15 AM
Floor Discussion
655
Thu, 8/4/2016,
8:30 AM -
10:20 AM
CC-W178b
New Advances in Clustering Algorithms — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Mary Akinyemi, University of Lagos
8:35 AM
On Assessing the Difficulty of a Clustering Problem: The Introduction of Sensitivity and Specificity to Cluster Analysis
—
Jonathon O'Brien
8:50 AM
A Contribution to Distance-Based Clustering in the Presence of Errors
—
Maha Bakoben, Imperial College London ; Tony Bellotti, Imperial College London ; Niall Adams, Imperial College London
9:05 AM
An Adaptive Association Test for Microbiome Data
—
Chong Wu
9:20 AM
Model-Based Clustering with Measurement Errors
—
Wanli Zhang, Oregon State University ; Yanming Di, Oregon State University
9:35 AM
A Simultaneous Variable Selection and Clustering Method for High-Dimensional Multinomial Regression Model
—
Sheng Ren, University of Cincinnati ; Jason Lu, Cincinnati Children's Hospital Research Foundation ; Emily Lei Kang, University of Cincinnati
9:50 AM
A Bootstrap Procedure for Classification Problems Based on Features and Observations Resampling
—
Junhong Liu, The University of Hong Kong
10:05 AM
Floor Discussion
656
Thu, 8/4/2016,
8:30 AM -
10:20 PM
CC-W182
Hypothesis Testing for Correlation and Dependence — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Jongphil Kim, Moffitt Cancer Center
8:35 AM
Dependence Discovery from Multimodal Data via Multiscale Graph Correlation
—
Cencheng Shen, Temple University ; Carey Priebe, The Johns Hopkins University ; Joshua Vogelstein, The Johns Hopkins University ; Mauro Maggioni, Duke University
8:50 AM
A Nonparametric Test of Independence Between Two Variables
—
Bin Li, Louisiana State University ; Qingzhao Yu, Louisiana State University Health Sciences Center
9:05 AM
Testing Statistical Significance of Canonical Correlation Coefficients
—
Yunjin Choi, Stanford University ; Robert Tibshirani, Stanford University ; Jonathan Taylor, Stanford University
9:35 AM
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model
—
Zhaoran Wang, Princeton ; Quanquan Gu, University of Virginia ; Han Liu, Princeton
9:50 AM
Hypothesis Tests for Hypervolumes Under K-Dimensional ROC Manifold
—
Rajarshi Dey, University of South Alabama
10:05 AM
Edgeworth Expansion for Summation of Symmetric Statistics and Its Application in Analyzing Massive Data
—
Liuhua Peng, Iowa State University ; Song Xi Chen, Peking University/Iowa State University
677 * !
Thu, 8/4/2016,
10:30 AM -
12:20 PM
CC-W175a
The Good, the Bad, and the Messy: Innovations in Analysis of Electronic Medical Records — Invited Papers
Section on Statistical Learning and Data Science , Committee on Applied Statisticians
Organizer(s): Ruth Etzioni, Fred Hutchinson Cancer Research Center, Suchi Saria, The Johns Hopkins University
Chair(s): Frank Yoon, Mathematica Policy Research
10:35 AM
Missing Data as a Causal and Probabilistic Problem
—
Ilya Shpitser, Johns Hopkins Computer Science
11:00 AM
Individualizing Prognosis of Disease Trajectories Using Longitudinal Electronic Medical Record Data: Application to Scleroderma
—
Suchi Saria, The Johns Hopkins University ; Peter Schulam, The Johns Hopkins University
11:25 AM
New Machine-Learning Approaches to Causal Inference
—
Cynthia Rudin, Duke University
11:50 AM
Discussant: Ruth Etzioni, Fred Hutchinson Cancer Research Center
12:10 PM
Floor Discussion
697
Thu, 8/4/2016,
10:30 AM -
12:20 PM
CC-W175b
Model Selection in High Dimensions: Theory and Inference — Contributed Papers
Section on Statistical Learning and Data Science
Chair(s): Oleg Melnikov, Rice University
10:35 AM
Consistency of Penalized Cross-Validation for Model Selection
—
Jieyi Jiang, The Ohio State University ; Steven N. MacEachern, The Ohio State University ; Yoonkyung Lee, The Ohio State University
10:50 AM
High-Dimensional Multivariate Repeated Measures Analysis with Unequal Covariance Matrices
—
Xiaoli Kong, University of Kentucky ; Solomon W. Harrar, University of Kentucky
11:05 AM
Model Selection Confidence Sets by Likelihood Ratio Testing
—
Chao Zheng ; Davide Ferrari, University of Melbourne ; Yuhong Yang, University of Minnesota
11:20 AM
Post-Selection Inference for E-MS Algorithm
—
Gang Xu, University of Miami ; J. Sunil Rao, University of Miami
11:35 AM
Sparse Clustering of High-Dimensional Gaussian Mixtures
—
Jing Ma, University of Pennsylvania ; Tony Cai, University of Pennsylvania ; Linjun Zhang, University of Pennsylvania
11:50 AM
High-Dimensional Matrix-Variate Linear Discriminant Analysis
—
Aaron Molstad, University of Minnesota ; Adam Rothman, University of Minnesota
12:05 PM
Pathwise Coordinate Optimization for Nonconvex Sparse Learning: Algorithm and Theory
—
Tuo Zhao, The Johns Hopkins University ; Han Liu, Princeton ; Tong Zhang, Rutgers University