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2014 Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model
Yalian Li, Hu Yang
J. Appl. Math. 2014: 1-6 (2014). DOI: 10.1155/2014/639070

Abstract

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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Yalian Li. Hu Yang. "Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model." J. Appl. Math. 2014 1 - 6, 2014. https://doi.org/10.1155/2014/639070

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07010705
MathSciNet: MR3193627
Digital Object Identifier: 10.1155/2014/639070

Rights: Copyright © 2014 Hindawi

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