Journal
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Volume 39, Issue 6, Pages 923-934Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610920902807911
Keywords
Liu estimator; Mean squared error matrix; Multicollinearity; Ridge regression estimator
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Funding
- Natural Science Foundation [2009BB6189]
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This article is concerned with the parameter estimation in linear regression model. To overcome the multicollinearity problem, a new two-parameter estimator is proposed. This new estimator is a general estimator which includes the ordinary least squares (OLS) estimator, the ridge regression (RR) estimator, and the Liu estimator as special cases. Necessary and sufficient conditions for the superiority of the new estimator over the OLS, RR, Liu estimators, and the two-parameter estimator proposed by Ozkale and Kaciranlar (2007) in the mean squared error matrix (MSEM) sense are derived. Furthermore, we obtain the estimators of the biasing parameters and give a numerical example to illustrate some of the theoretical results.
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