Bayesian inference for the generalized exponential distribution based on progressively censored data

Mohamed T. Madi; Mohammad Z. Raqab

doi:10.1080/03610920902855951

Bayesian inference for the generalized exponential distribution based on progressively censored data

Mohamed T. Madi, Mohammad Z. Raqab

Research output: Contribution to journal › Article › peer-review

20 Citations (Scopus)

Abstract

Based on a progressively Type-II censored sample, Bayesian estimation of the parameters as well as Bayesian prediction of the unobserved failure times from the generalized exponential (GE) distribution are studied. Importance sampling is used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are considered for predicting times to failure of units in multiple stages. A numerical simulation study involving three data sets is presented to illustrate the methods of estimation and prediction.

Original language	English
Pages (from-to)	2016-2029
Number of pages	14
Journal	Communications in Statistics - Theory and Methods
Volume	38
Issue number	12
DOIs	https://doi.org/10.1080/03610920902855951
Publication status	Published - Jan 2009

Keywords

Bayesian estimation
Bayesian prediction
Generalized exponential distribution
Gibbs and Metropolis sampling
Importance sampling
Maximum likelihood estimation

ASJC Scopus subject areas

Statistics and Probability

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10.1080/03610920902855951

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AB - Based on a progressively Type-II censored sample, Bayesian estimation of the parameters as well as Bayesian prediction of the unobserved failure times from the generalized exponential (GE) distribution are studied. Importance sampling is used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are considered for predicting times to failure of units in multiple stages. A numerical simulation study involving three data sets is presented to illustrate the methods of estimation and prediction.

KW - Bayesian estimation

KW - Bayesian prediction

KW - Generalized exponential distribution

KW - Gibbs and Metropolis sampling

KW - Importance sampling

KW - Maximum likelihood estimation

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JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 12

ER -

Bayesian inference for the generalized exponential distribution based on progressively censored data

Abstract

Keywords

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