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Testing equality of several distributions in separable metric spaces: A maximum mean discrepancy based approach

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  • Zhang, Jin-Ting
  • Guo, Jia
  • Zhou, Bu

Abstract

A new test for equal distributions of several high-dimensional samples in separable metric spaces, with its test statistic constructed based on maximum mean discrepancy, is proposed and studied. The asymptotic null and alternative distributions of the test statistic are established under some mild conditions. The new test is implemented via a three-cumulant matched chi-square approximation with the associated approximation parameters consistently estimated from the data. A new data-adaptive Gaussian kernel width selection method is also suggested. Good performance of the new test is illustrated by intensive simulation studies and a real data example of Gini index curves.

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  • Zhang, Jin-Ting & Guo, Jia & Zhou, Bu, 2024. "Testing equality of several distributions in separable metric spaces: A maximum mean discrepancy based approach," Journal of Econometrics, Elsevier, vol. 239(2).
    • Handle: RePEc:eee:econom:v:239:y:2024:i:2:s0304407622000859
    DOI: 10.1016/j.jeconom.2022.03.007
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Jin-Ting & Zhu, Tianming, 2024. "A fast and accurate kernel-based independence test with applications to high-dimensional and functional data," Journal of Multivariate Analysis, Elsevier, vol. 202(C).

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    More about this item

    Keywords

    Data heterogeneity; Multi-sample test for equal distributions; Maximum mean discrepancy; Three-cumulant matched chi-square-approximation; Gaussian kernel;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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