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Independence test for large sparse contingency tables based on distance correlation

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  • Zhang, Qingyang

Abstract

We propose a new approach to testing independence in a sparse contingency table based on distance correlation measure. We derive the explicit formula of the distance correlation between two categorical variables and suggest a simple permutation test for practical implementation.

Suggested Citation

  • Zhang, Qingyang, 2019. "Independence test for large sparse contingency tables based on distance correlation," Statistics & Probability Letters, Elsevier, vol. 148(C), pages 17-22.
    • Handle: RePEc:eee:stapro:v:148:y:2019:i:c:p:17-22
    DOI: 10.1016/j.spl.2018.12.010
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    References listed on IDEAS

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    1. Niklas Pfister & Peter Bühlmann & Bernhard Schölkopf & Jonas Peters, 2018. "Kernel‐based tests for joint independence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(1), pages 5-31, January.
    2. Székely, Gábor J. & Rizzo, Maria L., 2013. "The distance correlation t-test of independence in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 193-213.
    3. Liping Zhu & Kai Xu & Runze Li & Wei Zhong, 2017. "Projection correlation between two random vectors," Biometrika, Biometrika Trust, vol. 104(4), pages 829-843.
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    Cited by:

    1. Zhang, Jialin & Zhang, Zhiyi, 2024. "A normal test for independence via generalized mutual information," Statistics & Probability Letters, Elsevier, vol. 210(C).

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