CROSS-VALIDATORY CHOICE FOR THE NUMBER OF PRINCIPAL COMPONENTS IN PRINCIPAL COMPONENT REGRESSION

CROSS-VALIDATORY CHOICE FOR THE NUMBER OF PRINCIPAL COMPONENTS IN PRINCIPAL COMPONENT REGRESSION

We propose a cross-validatory method to choose the number of principal components in principal component regression based on the predicted error sum of squares. In the process of computation, we propose to use an approximation formula using a linear approximation based on the perturbation expansion....

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Veröffentlicht in:Journal of the Japanese Society of Computational Statistics 1996, Vol.9(1), pp.53-59
Hauptverfasser: Shin, Jae-Kyoung, Tanaka, Yutaka
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Sprache:eng
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cross-validation
number of principal components
principal component regression
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container_title Journal of the Japanese Society of Computational Statistics
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creator Shin, Jae-Kyoung
Tanaka, Yutaka
description We propose a cross-validatory method to choose the number of principal components in principal component regression based on the predicted error sum of squares. In the process of computation, we propose to use an approximation formula using a linear approximation based on the perturbation expansion. A numerical example is given to show the validity of the proposed method.
doi_str_mv 10.5183/jjscs1988.9.53
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source J-STAGE Free; EZB-FREE-00999 freely available EZB journals
subjects cross-validation
number of principal components
principal component regression
title CROSS-VALIDATORY CHOICE FOR THE NUMBER OF PRINCIPAL COMPONENTS IN PRINCIPAL COMPONENT REGRESSION
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