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A new stochastic restricted biased estimator under heteroscedastic or correlated error

Published online by Cambridge University Press:  22 February 2011

Mustafa Ismaeel Alheety
Mustafa Ismaeel Alheety*
Affiliation:
Department of Mathematics, Al-Anbar University, Ramadi, Iraq; alheety@yahoo.com
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Abstract

In this paper, under the linear regression model with heteroscedastic and/or correlated errors when the stochastic linear restrictions on the parameter vector are assumed to be held, a generalization of the ordinary mixed estimator (GOME), ordinary ridge regression estimator (GORR) and Generalized least squares estimator (GLSE) is proposed. The performance of this new estimator against GOME, GORR, GLS and the stochastic restricted Liu estimator (SRLE) [Yang and Xu, Statist. Papers50 (2007) 639–647] are examined in terms of matrix mean square error criterion. A numerical example is considered to illustrate the theoretical results.

Keywords

Heteroscedasticitygeneralized least squares estimatorstochastic restricted Liu estimator
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 15: Supplement: In honor of Marc Yor , 2011 , pp. 30 - 40
Copyright
© EDP Sciences, SMAI, 2011

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