Goodness-of-fit tests based on the distance between the Dirichlet process and its base measure

Luai Al Labadi, Mahmoud Zarepour

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)


    The Dirichlet process is a fundamental tool in studying Bayesian nonparametric inference. The Dirichlet process has several sum representations, where each one of these representations highlights some aspects of this important process. In this paper, we use the sum representations of the Dirichlet process to derive explicit expressions that are used to calculateKolmogorov, Lévy, and Cramér-von Mises distances between the Dirichlet process and its base measure. The derived expressions of the distance are used to select a proper value for the concentration parameter of the Dirichlet process. These tools are also used in a goodness-of-fit test. Illustrative examples and simulation results are included.

    Original languageEnglish
    Pages (from-to)341-357
    Number of pages17
    JournalJournal of Nonparametric Statistics
    Issue number2
    Publication statusPublished - 2014


    • Cramér-von Mises distance
    • Dirichlet process
    • Goodness-of-fit test
    • Kolmogorov distance
    • Lévy distance

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty


    Dive into the research topics of 'Goodness-of-fit tests based on the distance between the Dirichlet process and its base measure'. Together they form a unique fingerprint.

    Cite this