Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 124 - Recent Advances in Network Modeling and Visualizations
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: Korean International Statistical Society
Abstract #312546
Title: Network Based Variable Selection for High-Dimensional Longitudinal Data
Author(s): Cen Wu* and Fei Zhou
Companies: Kansas State University and Kansas State University
Keywords: Network-based variable selection; longitudinal data; genomics study
Abstract:

In high dimensional genomics studies, network—based penalization methods have achieved success since the performance of variable selection can be significantly improved with interconnections among genomic features being incorporated as network structures. Extensive network—constrained variable selection methods have been developed to accommodate a variety of outcome variables, such as the continuous disease phenotypes, discrete disease status and patients’ survival. However, its development in longitudinal studies is rather limited. In this study, we develop a novel network based variable selection method for longitudinal studies with high dimensional features. Extensive simulation studies have shown the superior performance of the proposed method over multiple alternatives. Analysis of a longitudinal study with SNP measurements has revealed its competitive practical performance.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2020 program