Improving statistical inference with uncertain non-sample prior information

Khan, Shahjahan and Memon, Muhammed Ashraf and Pratikno, Budi and Yunus, Rossita M. (2015) Improving statistical inference with uncertain non-sample prior information. Journal of Islamic Countries Society of Statistical Sciences, 1 (1). pp. 1-11. ISSN 2313-7800

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In the classical inference, the observed sample data is the only source of information. The Bayesian inferential methods assume prior distribution of the underlying model parameters to combine with sample data. Often non-sample prior information (NSPI) on the value of the model parameters is available from previous studies or expert knowledge which could be used along with the sample data to improve the quality of statistical inference. Obviously the NSPI is not always correct and hence there is uncertainty in the suspected value of the parameter. Any such uncertainty can be removed by conducting an appropriate statistical test, and the quality of statistical inference can be improved by including the outcome of the test in the inferential procedure. This paper provides the underlying methodology to illustrate the process and include an example to demonstrate its application.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: c. 2015 Islamic Countries Society of Statistical Sciences. Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 07 Dec 2015 07:01
Last Modified: 03 May 2017 00:03
Uncontrolled Keywords: regression model; uncertain non-sample prior information; restricted, preliminary test and shrinkage estimators; bias, relative efficiency, M and score tests, testing after pre-test, power of tests, correlated non-central bivariate chi-square distribution
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010499 Statistics not elsewhere classified
01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences

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