Linear model inference with non-sample prior information

Khan, Shahjahan and Pratikno, Budi and Yunus, Rossita M. (2012) Linear model inference with non-sample prior information. In: 12th Islamic Countries Conference on Statistical Sciences (ICCS 2012): Statistics for Everyone and Everywhere , 19-22 Dec 2012 , Doha, Qatar.

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Arguably the most widely used statistical technique is the linear model. Traditionally all classical inferences on the parameters of linear model are based exclusively on the
available sample data. Often valuable non-sample prior information on the value of the parameter of interest is available from the expert knowledge or previously conducted
studies. Inclusion of such information, in addition to the sample data, is likely to improve the quality of the inference. This paper uses both sample and non-sample information to define estimators of linear model and investigate their statistical properties. It also
incorporates the non-sample prior information in defining tests for a subset of parameters when information on the other subset is available. The comparisons of power of the tests are also explored under different conditions.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2013 Islamic Countries Society of Statistical Sciences.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 29 Oct 2013 00:46
Last Modified: 27 Jun 2017 05:41
Uncontrolled Keywords: pretest and shrinkage estimators; bias and mean squared error; pretest test; power and size of test; non-central bivariate t distribution
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010406 Stochastic Analysis and Modelling
01 Mathematical Sciences > 0104 Statistics > 010401 Applied Statistics
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

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