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Interpoint Distance Test of Homogeneity for Multivariate Mixture Models

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  • Yu Song
  • Reza Modarres

Abstract

Finite mixtures offer a rich class of distributions for modelling of a variety of random phenomena in numerous fields of study. Using the sample interpoint distances (IPDs), we propose the IPD‐test statistic for testing the hypothesis of homogeneity of mixture of multivariate power series distribution or multivariate normal distribution. We derive the distribution of the IPDs that are drawn from a finite mixture of the multivariate power series distribution and multivariate normal distribution. Based on the empirical distribution of the IPDs, we construct a bootstrap test of homogeneity for other multivariate finite mixture models. The IPD test is applied to mixture models for matrix‐valued distributions and a test of homogeneity for Wishart mixture is presented. Numerical comparisons show that IPD test has accurate type I errors and is more powerful in most multivariate cases than the expectation–maximization (EM) test and modified likelihood ratio test.

Suggested Citation

  • Yu Song & Reza Modarres, 2019. "Interpoint Distance Test of Homogeneity for Multivariate Mixture Models," International Statistical Review, International Statistical Institute, vol. 87(3), pages 613-638, December.
    • Handle: RePEc:bla:istatr:v:87:y:2019:i:3:p:613-638
    DOI: 10.1111/insr.12332
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    Cited by:

    1. Reza Modarres, 2020. "Graphical Comparison of High‐Dimensional Distributions," International Statistical Review, International Statistical Institute, vol. 88(3), pages 698-714, December.

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