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Activity Number: 522 - New Statistical Methods for Emerging Linked Data and Multi-View Data
Type: Invited
Date/Time: Thursday, August 6, 2020 : 1:00 PM to 2:50 PM
Sponsor: Biometrics Section
Abstract #309367
Title: Rank-Based Approaches for Integrative Analysis of Data from Multiple Sources
Author(s): Grace Yoon* and Irina Gaynanova
Companies: Texas A&M University and Texas A&M University
Keywords: Canonical correlation analysis; Copula; Polynomial interpolation; Time-varying; Zero-inflated

Collecting data on the same samples from multiple sources has become more common, which often results in data of mixed types such as continuous, binary and zero-inflated. Semiparametric rank-based approach to canonical correlation analysis (CCA) has been shown to have an excellent performance on dealing with mixed types of the data, however, it cannot be applied to data over time. To address this challenge, we propose time-varying CCA with rank-based approach that allows us to study how the associations between the two data sets change over time. For small sample size, we also propose data aggregation based on the change point. We further improve computational efficiency for rank-based approach using polynomial interpolation.

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

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