descriptionPublicationkeyboard_double_arrow_right Article 01 Sep 1995 English Publisher:Springer Science and Business Media LLCJournal:Statistics and Computing, volume 5, pages 227-235 (issn: 0960-3174, eissn: 1573-1375,

Authors: B. J. A. Mertens; T. Fearn; M. Thompson;

doi: 10.1007/bf00142664 , 10.1007/bf00162530

The efficient cross-validation of principal components applied to principal component regression

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Abstract

The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of omitting each observation in turn and repeating the principal component calculations is computationally costly. In this paper we present an efficient approach to leave-one-out cross-validation of principal components. This approach exploits the regular nature of leave-one-out principal component eigenvalue downdating. We derive influence statistics and consider the application to principal component regression.

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