Bayesian prediction of rainfall records using the generalized exponential distribution

Mohamed T. Madi, Mohammad Z. Raqab

    Research output: Contribution to journalArticlepeer-review

    23 Citations (Scopus)

    Abstract

    The Los Angeles rainfall data are found to fit well to the two-parameter generalized exponential (GE) distribution. A Bayesian parametric approach is described and used to predict the behavior of further rainfall records. Importance sampling is used to estimate the model parameters, and the Gibbs and Metropolis samplers are used to implement the prediction procedure.

    Original languageEnglish
    Pages (from-to)541-549
    Number of pages9
    JournalEnvironmetrics
    Volume18
    Issue number5
    DOIs
    Publication statusPublished - Aug 1 2007

    Keywords

    • Bayesian estimation
    • Bayesian prediction
    • Generalized exponential distribution
    • Gibbs and Metropolis sampling
    • Importance sampling
    • Record statistics

    ASJC Scopus subject areas

    • Statistics and Probability
    • Ecological Modelling

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