Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information

Khan, Shahjahan and Hoque, Zahirul and Saleh, A. K. Md. E. (2005) Estimation of the intercept parameter for linear regression model with uncertain non-sample prior information. Statistical Papers, 46 (3). pp. 379-395. ISSN 0932-5026

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[Abstract]: This paper considers alternative estimators of the intercept parameter of the linear regression model with normal error when uncertain non-sample prior information about the value of the slope parameter is available. The maximum likelihood, restricted, preliminary test and shrinkage estimators are considered. Based on their quadratic biases and mean square errors the relative
performances of the estimators are investigated. Both analytical and graphical methods are explored. None of the estimators is found to be uniformly dominating the others. However, if the non-sample prior information regarding the value of the slope is not too far from its true value, the shrinkage estimator of the intercept parameter dominates the rest of the estimators.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted version deposit in accordance with the copyright policy of the publisher. Copyright 2005 Springer. This is the author's version of the work. It is posted here with permission of the publisher for your personal use. No further distribution is permitted. The original publication is available at:
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:19
Last Modified: 09 Sep 2013 02:42
Uncontrolled Keywords: regression model, uncertain non-sample prior information, maximum likelihood, restricted, preliminary test, shrinkage estimators, bias, mean square error and relative efficiency
Fields of Research : 01 Mathematical Sciences > 0105 Mathematical Physics > 010599 Mathematical Physics not elsewhere classified
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080203 Computational Logic and Formal Languages
01 Mathematical Sciences > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified

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