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

Mohamed T. Madi, Mohammad Z. Raqab

    Research output: Contribution to journalArticlepeer-review

    19 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 languageEnglish
    Pages (from-to)2016-2029
    Number of pages14
    JournalCommunications in Statistics - Theory and Methods
    Volume38
    Issue number12
    DOIs
    Publication statusPublished - 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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