Comparison of estimators of means based on p-samples from multivariate Student-t population

Khan, Shahjahan and Saleh, A. K. Md. Ehsanes (1998) Comparison of estimators of means based on p-samples from multivariate Student-t population. Communications in Statistics: Theory and Methods, 27 (1). pp. 193-210. ISSN 0361-0926


Different strategies for the estimation of the mean for samples from p multivariate Student-t populations in presence of uncertain prior information on the value of the mean in the form of a null hypothesis is investigated. Based on the likelihood function and the uncertain prior information, four different estimators, namely, the unrestricted, restricted, pre-test and shrinkage esti¬mators of the location parameter for a location-scale model are defined. The expressions for the bias, mean square error and risk under quadratic loss function are obtained for each of the estimators. Comparison of the performances of the estimators are made with respect to the mean square error, relative efficiency and quadratic risk under the null as we as the alternative hypotheses. Conclusions regarding the relative performance, dominance picture and inadmissibility of the estimators are also provided.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 30 Nov 2007 11:47
Last Modified: 24 Feb 2016 01:58
Uncontrolled Keywords: uncertain prior information; Student-t; normal and inverted gamma distributions; preliminary test and shrinkage estimators; bias; risk under quadratic loss
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
01 Mathematical Sciences > 0104 Statistics > 010404 Probability Theory
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
Identification Number or DOI: 10.1080/03610929808832660

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