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The small sample properties of the restricted principal component regression estimator in linear regression model

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  • Jibo Wu

Abstract

In regression analysis, to deal with the problem of multicollinearity, the restricted principal components regression estimator is proposed. In this paper, we compared the restricted principal components regression estimator, the principal components regression estimator, and the ordinary least-squares estimator with each other under the Pitman's closeness criterion. We showed that the restricted principal components regression estimator is always superior to the principal components regression estimator, under certain conditions the restricted principal components regression estimator is superior to the ordinary least-squares estimator under the Pitman's closeness criterion and under certain conditions the principal components regression estimator is superior to the ordinary least-squares estimator under the Pitman's closeness criterion.

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  • Jibo Wu, 2017. "The small sample properties of the restricted principal component regression estimator in linear regression model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(4), pages 1661-1667, February.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:4:p:1661-1667
    DOI: 10.1080/03610926.2015.1024867
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