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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.

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