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2016 ; 17
(3
): 468-83
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Canonical variate regression
#MMPMID26861909
Luo C
; Liu J
; Dey DK
; Chen K
Biostatistics
2016[Jul]; 17
(3
): 468-83
PMID26861909
show ga
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.
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