Estimation of slope for linear regression model with uncertain prior information and student-t error

Khan, Shahjahan and Saleh, A. K. Md. E. (2008) Estimation of slope for linear regression model with uncertain prior information and student-t error. Communications in Statistics: Theory and Methods, 37 (16). pp. 2564-2581. ISSN 0361-0926

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This article considers estimation of the slope parameter of the linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope. Incorporating uncertain non sample prior information with the sample data the unrestricted, restricted, preliminary test, and shrinkage estimators are defined. The performances of the estimators are compared based on the criteria of unbiasedness and mean squared errors. Both analytical and graphical methods are explored. Although none of the estimators is uniformly superior to the others, if the non sample information is close to its true value, the shrinkage estimator over performs the rest of the estimators.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Author version deposited in accordance with the copyright policy of publisher.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 04 Nov 2008 02:07
Last Modified: 22 Jun 2018 01:17
Uncontrolled Keywords: bias; mean square error and relative efficiency; incomplete beta ratio; mixture distribution of normal and inverted gamma; multiple regression model; non central chi-square and F distributions; preliminary test and shrinkage estimators; student-t errors
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010406 Stochastic Analysis and Modelling
01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory
01 Mathematical Sciences > 0102 Applied Mathematics > 010203 Calculus of Variations, Systems Theory and Control Theory
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
Identification Number or DOI: 10.1080/03610920802040399

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