title: Canonical variate regression.
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 [Formula: see text] intercross mice study and an alcohol dependence study.
authors: Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
authors: Chongliang
authors: C
authors: Luo
authors: Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore 169856, Singapore.
authors: Jin
authors: J
authors: Liu
authors: Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
authors: Dipak K
authors: DK
authors: Dey
authors: Department of Statistics, University of Connecticut, Storrs, CT 06269, USA kun.chen@uconn.edu.
authors: Kun
authors: K
authors: Chen
mesh_terms: D000437:Alcoholism / Q000235:genetics; D000465:Algorithms; D000818:Animals; D001835:Body Weight / Q000235:genetics; D003627:Data Interpretation, Statistical; D006801:Humans; D051379:Mice; D015233:Models, Statistical; D012044:Regression Analysis; D000069553:Supervised Machine Learning
keywords: Canonical correlation analysis; Integrative analysis; Reduced-rank regression; Supervised learning