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Testing equivalence of multinomial distributions — A constrained bootstrap approach

Author

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  • Bastian, P.
  • Dette, H.
  • Koletzko, L.

Abstract

In this paper we develop a novel bootstrap test for the comparison of two multinomial distributions. The two distributions are called equivalent or similar if a norm of the difference between the class probabilities is smaller than a given threshold. In contrast to most of the literature our approach does not require differentiability of the norm and is in particular applicable for the maximum- and L1-norm.

Suggested Citation

  • Bastian, P. & Dette, H. & Koletzko, L., 2024. "Testing equivalence of multinomial distributions — A constrained bootstrap approach," Statistics & Probability Letters, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:stapro:v:206:y:2024:i:c:s0167715223002237
    DOI: 10.1016/j.spl.2023.109999
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    References listed on IDEAS

    as
    1. Holger Dette & Kathrin Möllenhoff & Stanislav Volgushev & Frank Bretz, 2018. "Equivalence of Regression Curves," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 711-729, April.
    2. Zheng Fang & Andres Santos, 2019. "Inference on Directionally Differentiable Functions," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 377-412.
    3. Ostrovski, Vladimir, 2018. "Testing equivalence to families of multinomial distributions with application to the independence model," Statistics & Probability Letters, Elsevier, vol. 139(C), pages 61-66.
    4. Ostrovski, Vladimir, 2017. "Testing equivalence of multinomial distributions," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 77-82.
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