A Bayesian nonparametric goodness of fit test for right censored data based on approximate samples from the beta-Stacy process

Luai Al Labadi, Mahmoud Zarepour

    Research output: Contribution to journalArticlepeer-review

    13 Citations (Scopus)

    Abstract

    In recent years, Bayesian nonparametric statistics has received extraordinary attention. The beta-Stacy process, a generalization of the Dirichlet process, is a fundamental tool in studying Bayesian nonparametric statistics. In this article, we derive a simple, yet efficient, way to simulate the beta-Stacy process. We compare the efficiency of the new approximation to several other well-known approximations, and we demonstrate a significant improvement. Using the Kolmogorov distance and samples from the beta-Stacy process, a Bayesian nonparametric goodness of fit test is proposed. The proposed test is very general in the sense that it can be applied to censored and non-censored observations. Some illustrative examples are included.

    Original languageEnglish
    Pages (from-to)466-487
    Number of pages22
    JournalCanadian Journal of Statistics
    Volume41
    Issue number3
    DOIs
    Publication statusPublished - Sep 1 2013

    Keywords

    • Beta-Stacy process
    • Ferguson and Klass representation
    • Goodness of fit test
    • Kolmogorov distance
    • Wolpert and Ickstadt representation

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty

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