CANONICAL CORRELATION ANALYSIS THROUGH LINEAR MODELING
- 주제(키워드) canonical correlation , least squares , multivariate regression , sufficient dimension reduction , variable selection
- 등재 SCIE, SCOPUS
- 발행기관 WILEY-BLACKWELL
- 발행년도 2014
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000091297
- 본문언어 영어
- Published As http://dx.doi.org/10.1111/anzs.12057
초록/요약
In this paper, we introduce linear modeling of canonical correlation analysis, which estimates canonical direction matrices by minimising a quadratic objective function. The linear modeling results in a class of estimators of canonical direction matrices, and an optimal class is derived in the sense described herein. The optimal class guarantees several of the following desirable advantages: first, its estimates of canonical direction matrices are asymptotically efficient; second, its test statistic for determining the number of canonical covariates always has a chi-squared distribution asymptotically; third, it is straight forward to construct tests for variable selection. The standard canonical correlation analysis and other existing methods turn out to be suboptimal members of the class. Finally, we study the role of canonical variates as a means of dimension reduction for predictors and responses in multivariate regression. Numerical studies and data analysis are presented.
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