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Journal of Applied Mathematics
Volume 2014, Article ID 639070, 6 pages
Research Article

Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model

Department of Statistics and Actuarial Science, Chongqing University, Chongqing 401331, China

Received 15 January 2014; Accepted 8 March 2014; Published 2 April 2014

Academic Editor: Li Weili

Copyright © 2014 Yalian Li and Hu Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.