|
Activity Number:
|
198
- Highlights from STAT
|
|
Type:
|
Topic-Contributed
|
|
Date/Time:
|
Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
|
|
Sponsor:
|
International Statistical Institute
|
|
Abstract #317608
|
|
|
Title:
|
Integrative Analysis of Longitudinal High-Dimensional Data with Time-Lagged Associations
|
|
Author(s):
|
Yuping Zhang*
|
|
Companies:
|
University of Connecticut
|
|
Keywords:
|
dimension reduction;
lagged association;
principal trend;
shifted basis matrix;
time-course
|
|
Abstract:
|
Time-lagged correlations widely exist in the real world. Lagged principal trend analysis (LAPTA) method is developed for integrating longitudinal high-dimensional datasets with time-lagged temporal associations. Specifically, given longitudinal high-dimensional datasets of two cohorts, LAPTA can extract time-lagged associations and identify relevant features. The practical merits of LAPTA are demonstrated through simulations and applications to host transcriptional response to influenza A infection data.
|
Authors who are presenting talks have a * after their name.
