Canonical variate regression

Chongliang Luo, Jin Liu, Dipak K. Dey, Kun Chen

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an F-2 intercross mice study and an alcohol dependence study.

Original languageEnglish
Pages (from-to)468-483
Number of pages16
JournalBiostatistics
Volume17
Issue number3
DOIs
StatePublished - Jul 1 2016

Keywords

  • Canonical correlation analysis
  • Integrative analysis
  • Reduced-rank regression
  • Supervised learning

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