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Efficiency of two classes of stochastic restricted almost unbiased type principal component estimators in linear regression model

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  • Yalian Li

Abstract

In this paper, we introduce two new classes of estimators called the stochastic restricted almost unbiased ridge-type principal component estimator (SRAURPCE) and the stochastic restricted almost unbiased Liu-type principal component estimator (SRAURPCE) to overcome the well-known multicollinearity problem in linear regression model. For the two cases when the restrictions are true and not true, necessary and sufficient conditions for the superiority of the proposed estimators are derived and compared, respectively. Furthermore, a Monte Carlo simulation study and a numerical example are given to illustrate the performance of the proposed estimators.

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  • Yalian Li, 2018. "Efficiency of two classes of stochastic restricted almost unbiased type principal component estimators in linear regression model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(4), pages 793-804, February.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:4:p:793-804
    DOI: 10.1080/03610926.2015.1137598
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