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Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic on partly not identically distributed data

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  • Daniel Gaigall

Abstract

The established Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic is investigated for partly not identically distributed data. Surprisingly, it turns out that the statistic has the well-known distribution-free limiting null distribution of the classical criterion under standard regularity conditions. An application is testing goodness-of-fit for the regression function in a non parametric random effects meta-regression model, where the consistency is obtained as well. Simulations investigate size and power of the approach for small and moderate sample sizes. A real data example based on clinical trials illustrates how the test can be used in applications.

Suggested Citation

  • Daniel Gaigall, 2022. "Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic on partly not identically distributed data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(12), pages 4006-4028, May.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:12:p:4006-4028
    DOI: 10.1080/03610926.2020.1805767
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    Cited by:

    1. Drautzburg, Thorsten & Wright, Jonathan H., 2023. "Refining set-identification in VARs through independence," Journal of Econometrics, Elsevier, vol. 235(2), pages 1827-1847.

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