Estimation of the parameters of two parallel regression lines under uncertain prior information

Khan, Shahjahan (2003) Estimation of the parameters of two parallel regression lines under uncertain prior information. Biometrical Journal, 45 (1). pp. 73-90. ISSN 0323-3847


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The problem of parallelism for bi-linear regression lines arises in many real life investigations. For two linear regression models with normal errors, the estimation of the
slope as well as the intercept parameters is considered when it is apriori suspected that the two lines are parallel. Three different estimators are defined by using both the sample data and the non-sample uncertain prior information. The relative performances of the unrestricted, restricted and preliminary test estimators are investigated based on the analysis of the bias, and risk functions under quadratic loss. An example based on a medical study is used to illustrate the method.

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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.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 11 Oct 2007 00:37
Last Modified: 02 May 2017 00:10
Uncontrolled Keywords: two parallel regression lines; non-sample uncertain prior information; multivariate normal distribution; central and non-central chi-squared and F-distributions; maximum likelihood; restricted and preliminary test estimators; bias and quadratic risk
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory
Identification Number or DOI: 10.1002/bimj.200290017

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