Shrinkage pre-test estimator of the intercept parameter for a regression model with mulativariate student-t errors

Khan, Shahjahan and Saleh, A. K. Md. Ehsanes (1997) Shrinkage pre-test estimator of the intercept parameter for a regression model with mulativariate student-t errors. Biometrical Journal, 39 (2). pp. 131-147. ISSN 0323-3847

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Abstract

[Abstract]: In presence of an uncertain prior information about the slope parameter, the estimation of the intercept of a simple regression model with a multivariate Student-t error distribution is investigated. The unrestricted, restricted and preliminary test maximum likelihood estimators are defined. The expressions for the bias and the mean square error of the three estimators are provided and the relative efficiencies are analysed. A maximin criterion is established, and graphs and tables are constructed for different number of degrees of freedom (D.F.) as well as sample sizes. A criterion to select optimal significance levelis also discussed.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher. Pre-print version of article, as made available here, differs in title from the Published version. Preprint title: Pre-test estimators of the intercept for a regression model with mulativariate student-t errors.
Depositing User: Professor Shahjahan Khan
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:37
Last Modified: 02 Jul 2013 22:36
Uncontrolled Keywords: regression model, uncertain prior information, preliminary test estimator, multivariate t-distribution, inverted Gamma and non-central F distributions, incomplete Beta distribution, unrestricted and restricted maximum likelihood estimators, mean square errors, relative efficiency, maximin rule
Fields of Research (FOR2008): 01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory
01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
Identification Number or DOI: doi: 10.1002/bimj.4710390202
URI: http://eprints.usq.edu.au/id/eprint/1213

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