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MODELS BASED ON PARTIAL INFORMATION ABOUT SURVIVAL AND HAZARD GRADIENT

Published online by Cambridge University Press:  19 August 2010

Majid Asadi ,
Somayeh Ashrafi ,
Nader Ebrahimi  and
Ehsan S. Soofi
Majid Asadi
Affiliation:
Department of Statistics, University of Isfahan, Isfahan, 81744, Iran E-mail: m.asadi@sci.ui.ac.ir
Somayeh Ashrafi
Affiliation:
Department of Statistics, University of Isfahan, Isfahan, 81744, Iran E-mail: m.asadi@sci.ui.ac.ir
Nader Ebrahimi
Affiliation:
Division of Statistics, Northern Illinois University, DeKalb, IL 60155 E-mail: nader@math.niu.edu
Ehsan S. Soofi
Affiliation:
Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53201 E-mail: esoofi@uwm.edu
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Abstract

This article develops information optimal models for the joint distribution based on partial information about the survival function or hazard gradient in terms of inequalities. In the class of all distributions that satisfy the partial information, the optimal model is characterized by well-known information criteria. General results relate these information criteria with the upper orthant and the hazard gradient orderings. Applications include information characterizations of the bivariate Farlie–Gumbel–Morgenstern, bivariate Gumbel, and bivariate generalized Gumbel, for which no other information characterization are available. The generalized bivariate Gumbel model is obtained from partial information about the survival function and hazard gradient in terms of marginal hazard rates. Other examples include dynamic information characterizations of the bivariate Lomax and generalized bivariate Gumbel models having marginals that are transformations of exponential such as Pareto, Weibull, and extreme value. Mixtures of bivariate Gumbel and generalized Gumbel are obtained from partial information given in terms of mixtures of the marginal hazard rates.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 24 , Issue 4 , October 2010 , pp. 561 - 584
Copyright
Copyright © Cambridge University Press 2010

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