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Bayes estimation for the Marshall–Olkin bivariate Weibull distribution

Debasis Kundu and Arjun K. Gupta

Computational Statistics & Data Analysis, 2013, vol. 57, issue 1, 271-281

Abstract: In this paper, we consider the Bayesian analysis of the Marshall–Olkin bivariate Weibull distribution. It is a singular distribution whose marginals are Weibull distributions. This is a generalization of the Marshall–Olkin bivariate exponential distribution. It is well known that the maximum likelihood estimators of the unknown parameters do not always exist. The Bayes estimators are obtained with respect to the squared error loss function and the prior distributions allow for prior dependence among the components of the parameter vector. If the shape parameter is known, the Bayes estimators of the unknown parameters can be obtained in explicit forms under the assumptions of independent priors. If the shape parameter is unknown, the Bayes estimators cannot be obtained in explicit forms. We propose to use the importance sampling method to compute the Bayes estimators and also to construct associated credible intervals of the unknown parameters. The analysis of one data set is performed for illustrative purposes. Finally we indicate the analysis of data sets obtained from series and parallel systems.

Keywords: Bivariate exponential model; Maximum likelihood estimators; Importance sampling; Prior distribution; Posterior analysis; Credible intervals (search for similar items in EconPapers)
Date: 2013
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Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:271-281