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Two classes of almost unbiased type principal component estimators in linear regression model. (English) Zbl 1407.62259

Summary: This paper is concerned with the parameter estimator in linear regression model. To overcome the multicollinearity problem, two new classes of estimators called the almost unbiased ridge-type principal component estimator (AURPCE) and the almost unbiased Liu-type principal component estimator (AULPCE) are proposed, respectively. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a Monte Carlo simulation study is given to illustrate the performance of the proposed estimators.

MSC:

62J05 Linear regression; mixed models
62H25 Factor analysis and principal components; correspondence analysis
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