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Minimum dynamic discrimination information models

Published online by Cambridge University Press:  14 July 2016

Majid Asadi*
Affiliation:
University of Isfahan
Nader Ebrahimi*
Affiliation:
Northern Illinois University
G. G. Hamedani*
Affiliation:
Marquette University
Ehsan S. Soofi*
Affiliation:
University of Wisconsin-Milwaukee
*
Postal address: Department of Statistics, University of Isfahan, Isfahan, 81744, Iran. Email address: [email protected]
∗∗Postal address: Division of Statistics, Northern Illinois University, DeKalb, IL 60155, USA. Email address: [email protected]
∗∗∗Postal address: Department of Mathematics, Statistics and Computer Science, Marquette University, PO Box 1881, Milwaukee, WI 53201-1881, USA. Email address: [email protected]
∗∗∗∗Postal address: School of Business Administration, University of Wisconsin-Milwaukee, PO Box 741, Milwaukee, WI 53201, USA. Email address: [email protected]
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Abstract

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In this paper, we introduce the minimum dynamic discrimination information (MDDI) approach to probability modeling. The MDDI model relative to a given distribution G is that which has least Kullback-Leibler information discrepancy relative to G, among all distributions satisfying some information constraints given in terms of residual moment inequalities, residual moment growth inequalities, or hazard rate growth inequalities. Our results lead to MDDI characterizations of many well-known lifetime models and to the development of some new models. Dynamic information constraints that characterize these models are tabulated. A result for characterizing distributions based on dynamic Rényi information divergence is also given.

Type
Research Papers
Copyright
© Applied Probability Trust 2005 

References

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