Inference for the generalized Rayleigh distribution based on progressively censored data

Mohammad Z. Raqab, Mohamed T. Madi

    Research output: Contribution to journalArticlepeer-review

    35 Citations (Scopus)

    Abstract

    In this paper, and based on a progressive type-II censored sample from the generalized Rayleigh (GR) distribution, we consider the problem of estimating the model parameters and predicting the unobserved removed data. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The Gibbs and Metropolis samplers are used to predict the life lengths of the removed units in multiple stages of the progressively censored sample. Artificial and real data analyses have been performed for illustrative purposes.

    Original languageEnglish
    Pages (from-to)3313-3322
    Number of pages10
    JournalJournal of Statistical Planning and Inference
    Volume141
    Issue number10
    DOIs
    Publication statusPublished - Oct 2011

    Keywords

    • Bayesian estimation
    • Bayesian prediction
    • Generalized rayleigh distribution
    • Gibbs and metropolis sampling
    • Importance sampling
    • Maximum likelihood estimation

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty
    • Applied Mathematics

    Fingerprint

    Dive into the research topics of 'Inference for the generalized Rayleigh distribution based on progressively censored data'. Together they form a unique fingerprint.

    Cite this