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

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

Author(s) -

Yalian Li,

Hu Yang

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/639070

Subject(s) - multicollinearity , estimator , mathematics , principal component regression , mean squared error , principal component analysis , statistics , bias of an estimator , minimum variance unbiased estimator , best linear unbiased prediction , efficient estimator , linear regression , stein's unbiased risk estimate , monte carlo method , linear model , computer science , artificial intelligence , selection (genetic algorithm)

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

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