Legend:
CC = Baltimore Convention Center,
H = Hilton Baltimore
* = applied session ! = JSM meeting theme
184
Mon, 7/31/2017,
11:35 AM -
12:20 PM
CC-Halls A&B
SPEED: Variable Selection and Networks — Contributed Poster Presentations
Section on Statistical Learning and Data Science
Chair(s): Jessi Cisewski, Yale University
The Speed portion will take place during Session 214519
2:
A Robust Model-Free Feature Screening Method for Ultrahigh-Dimensional Data
—
Jingnan Xue, Texas A&M University ; Faming Liang, University of Florida
3:
Dynamic Latent Factor Modeling of UN Voting Networks
—
Bomin Kim, Pennsylvania State University ; Xiaoyue Niu, Penn State University ; David Hunter, The Pennsylvania State University ; Xun Cao, The Pennsylvania State University
4:
Variable Selection via Phony Variables
—
Wenhao Hu, North Carolina State University ; Eric Laber, North Carolina State University ; Leonard Stefanski, North Carolina State University
5:
Impact of Divergence of Training and Testing Sets on Predictive Risk and Measure of Model Complexity
—
Jieyi Jiang, Ohio State University ; Yoonkyung Lee, The Ohio State University ; Steven MacEachern, The Ohio State University
6:
Bayesian Adjustment for Confounding When Estimating Average Causal Effects for Time-To-Event Outcomes
—
Li Xu ; Arnold Stromberg, Department of Statistics, University of Kentucky ; Chi Wang, Cancer Biostatistics, University of Kentucky
7:
Efficient causal structure learning in high dimensions
—
Arjun Sondhi, University of Washington ; Ali Shojaie, University of Washington
8:
Assessing Variable Importance Nonparametrically Using Machine Learning Techniques
—
Brian Williamson, University of Washington ; Marco Carone, University of Washington Department of Biostatistics ; Noah Simon, University of Washington ; Peter Gilbert, Fred Hutchinson Cancer Research Center
9:
Varying-Coefficient Models for Dynamic Networks
—
Jihui Lee, Columbia University ; Gen Li, Columbia University ; James D. Wilson, University of San Francisco
10:
Parsimonious and Efficient Construction of Composite Likelihood Equations by L1-Penalization
—
Zhendong Huang
11:
Cross Validation for Penalized M-Estimation with a Case-Weight Adjusted Solution Path
—
Shanshan Tu, The Ohio State University ; Yunzhang Zhu, Ohio State University ; Yoonkyung Lee, The Ohio State University
12:
Finite Sample Estimation in General Vector Autoregressive Processes
—
Mohamad Kazem Shirani Faradonbeh, University of Michigan ; Ambuj Tewari, University of Michigan ; George Michailidis, University of Florida
13:
A Regularization Method for Detecting Differential Item Functioning Under the Framework of Generalized Linear Models
—
Jing Jiang, Boston College ; Zhushan Li, Boston College
14:
Collaborative Spectral Clustering in Attributed Networks
—
Xiaodong Jiang ; Pengsheng Ji, University of Georgia
15:
Structural Discovery in Temporal Networks
—
Shaojun Zhang, University of Florida ; George Michailidis, University of Florida
16:
Variable Selection for High-Dimensional Data via Generalized Penalty
—
Mingwei Sun, University of Alabama ; Pu Patrick Wang, University of Alabama
17:
Neighborhood Selection with Application to Social Networks
—
Nana Wang ; Wolfgang Polonik, University of California, Davis
18:
Detection of Treatment Effect After Variable Selection Under Model Misspecification
—
Jingshen Wang, University of Michigan ; Xuming He, University of Michigan
19:
Fostering Undergraduate Data Science
—
Mark Ward, Purdue University ; Fulya Gokalp Yavuz, Purdue University and Yildiz Technical University
20:
Model-Based Community Detection for Networks with Node Covariates
—
Boang Liu, University of Michigan ; Ji Zhu, University of Michigan
