Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416618 | Computational Statistics & Data Analysis | 2007 | 12 Pages |
Abstract
Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures redundant information in the criterion variables in a most parsimonious way. A ridge type of regularization was introduced in RA to deal with the multicollinearity problem among the predictor variables. The regularized linear RA was extended to nonlinear RA using a kernel method to enhance the predictability. The usefulness of the proposed procedures was demonstrated by a Monte Carlo study and through the analysis of two real data sets.
Keywords
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Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Yoshio Takane, Heungsun Hwang,