Contributed Papers

Advanced Research in High Dimensional Regression

Wed, Aug 6, 8:30 AM - 10:20 AM Room CC-103A Music City Center

Danh NguyenChair

Biometrics Section

About this session

This session will have presenters showing latest research in high dimensional regression ranging from new methods for handling longitudinal data, diurnal data, spatial data with high dimensional covariates, etc..

7 Presentations

WITHDRAWN: Debiased network constrained sparse group lasso with applications to high-dimension longitudinal omics data

8:30 AM - 10:20 AM

Co-authors: Steven Broll (Cornell University), Martin Wells (Cornell University), Sumanta Basu (Cornell University Department of Statistics and Data Science), Myung Hee Lee (Weill Cornell Medicine)

Omics High Dimensional Metabolomics Regression

Doubly regularized generalized linear models for spatial data with high-dimensional covariates

8:35 AM - 8:50 AM

Co-authors: Arjun Sondhi, Si Cheng (University of Washington), Arjun Sondhi, Ali Shojaie (University of Washington)

high-dimensional data penalized regression spatial data networks

Flexible Modeling Framework for Self-Exciting Diurnal Processes with Applications to Smartphone Use

8:50 AM - 9:05 AM

Co-authors: Ryan Xie, Ian Barnett (University of Pennsylvania), Ryan Xie

mobile health longitudinal and correlated data point processes diurnal patterns event data mental health

Functional dynamic models with functional and scalar predictors prone to measurement errors.

9:05 AM - 9:20 AM

Co-authors: Mercy Oladuti, Mercy Oladuti, Roger S Zoh (Indiana University), Xue Lan (Oregon State University), See Ling Loy (Duke-NUS Medical School), Carmen Tekwe (Indiana University)

Functional data Glycemic dynamics Measurement Error Optimal Meal Timing Physical Activity Meal Type

Inference on the Significance of Modalities in Multimodal Generalized Linear Models

9:20 AM - 9:50 AM

Co-authors: Wanting Jin, Wanting Jin, Quefeng Li (University of North Carolina Chapel Hill), Guorong Wu (UNC)

High-dimensional inference Multimodal data,Relative Entropy Sure Independence Screening

Targeted Learning of Heterogeneous Sources by Informative Feature Sharing

9:50 AM - 10:05 AM

Co-authors: Jie Hu (University of Pennsylvania), Yudong Wang (University of Pennsylvania, Perelman School of Medicine), Jie Hu (University of Pennsylvania), Tingyin Wang, Yumou Qiu (Peking University), Yang Ning (Cornell University), Yong Chen (University of Pennsylvania, Perelman School of Medicine)

Generalized linear model heterogeneity informative support negative transfer robust transfer learning

Tractable Conditional Density Estimation using Logistic Gaussian Process

10:05 AM - 10:20 AM

Co-authors: Indrajit Ghosh (Texas AM University), Debdeep Pati (University of Wisconsin-Madison), Dipankar Bandyopadhyay (Virginia Commonwealth University), Jaehoan Kim (Duke University)

Density estimation Posterior Contraction Spline Scalable approximation