A New Stochastic Mixed Liu Estimator in Linear Regression Model

被引：0

作者：

Zuo, Weibing ^{[1
]}

Cheng, Peng ^{[1
]}

机构：

[1] N China Univ Water Conservancy & Hydroelect, Coll Math & Informat Sci, Zhengzhou 450011, Henan, Peoples R China

来源：

PROCEEDINGS OF THE THIRD INTERNATIONAL WORKSHOP ON MATRIX ANALYSIS AND APPLICATIONS, VOL 3 | 2009年

关键词：

Linear regression model; Ordinary mixed estimator; Liu estimator; Stochastic restricted Liu estimator; Alternative stochastic restricted Liu estimator; Mean square error matrix;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper is concerned with the parameter estimation in linear regression model with additional stochastic linear restrictions. To overcome the multicollinearity problem, a new stochastic mixed Liu estimator is proposed and its efficiency is discussed. The new estimator is a generalization of the ordinary mixed estimator (OME) (Theil and Goldberger, 1961) and Liu estimator (LE) (Liu K, 1993). Necessary and sufficient conditions for the superiority of the new stochastic mixed Liu estimator over the OME, the Liu estimator, the estimator proposed by Hubert and Wijekoon (2006) and the estimator proposed by Hu Yang and Jianwen Xu (2007) in the mean squared error matrix (MSEM) sense are derived. Finally, a numerical example (Gruber, 1998) is given to illustrate some of the theoretical results.

引用

页码：386 / 390

页数：5

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