Bayesian prediction of temperature records using the Pareto model

Mohamed T. Madi, Mohamed Z. Raqab

    Research output: Contribution to journalArticlepeer-review

    45 Citations (Scopus)

    Abstract

    Statistical prediction of record values has potential environmental applications dealing, for example, with abrupt climate jumps, such as the prediction of rainfall extremes, highest water levels and sea surface or air record temperatures. In this article, and on the basis of observed Pareto records drawn from a sequential sample of independent and identically distributed random variables, we address the problem of Bayesian prediction of future records. The Bayesian predictive distribution is developed for future records and the corresponding highest posterior density (HPD)-prediction intervals are established. A data set representing the record values of average July temperatures in Neuenburg, Switzerland, is used to illustrate the proposed prediction procedure's environmental application.

    Original languageEnglish
    Pages (from-to)701-710
    Number of pages10
    JournalEnvironmetrics
    Volume15
    Issue number7
    DOIs
    Publication statusPublished - Nov 2004

    Keywords

    • Bayesian prediction
    • Gibbs sampling
    • HPD-prediction intervals
    • Pareto distribution
    • Predictive density
    • Record statistics
    • Record temperatures

    ASJC Scopus subject areas

    • Statistics and Probability
    • Ecological Modelling

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