Activity Number:
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198
- Highlights from STAT
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Type:
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Topic-Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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International Statistical Institute
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Abstract #317608
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Title:
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Integrative Analysis of Longitudinal High-Dimensional Data with Time-Lagged Associations
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Author(s):
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Yuping Zhang*
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Companies:
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University of Connecticut
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Keywords:
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dimension reduction;
lagged association;
principal trend;
shifted basis matrix;
time-course
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.