Classical and Bayesian prediction for multivariate simple regression model

Khan, Shahjahan (2014) Classical and Bayesian prediction for multivariate simple regression model. Journal of Applied Probability and Statistics, 9 (1). 67- 78. ISSN 1930-6792

Abstract

Both Bayesian and classical approaches are used to derive the prediction distribution of a set of future responses, conditional on another set of independent realized responses, from the multivariate simple regression model in this paper. The errors from both the performed and future experiments are assumed to be identically and independently distributed as multivariate normal variables. Conditional on the realized responses, the future unrealized responses follow a matrix T distribution. The shape parameter of the prediction distribution depends on the size of the realized sample, and the dimension of the regression parameters in the model. The prediction distribution obtained by both the classical method and Bayesian method under uniform prior is the same.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Full Reference: Khan, S (2014). Classical and Bayesian prediction for multivariate simple regression model. Journal of Applied Probability and Statistics. Vol. 9(1), 67-78
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 19 Feb 2015 04:31
Last Modified: 07 Jul 2016 05:00
Uncontrolled Keywords: multivariate normal and Student-t distributions; matrix normal, matrix gamma and matrix T distributions; matrix integration; invariant differentials; uniform prior; and prediction distribution.
Fields of Research : 01 Mathematical Sciences > 0104 Statistics > 010499 Statistics not elsewhere classified
01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory
01 Mathematical Sciences > 0104 Statistics > 010404 Probability Theory
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences
URI: http://eprints.usq.edu.au/id/eprint/26543

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