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Testing equivalence of multinomial distributions

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  • Ostrovski, Vladimir

Abstract

We introduce the smooth total variation distance as a natural choice to establish equivalence. We propose asymptotic and bootstrap-based tests for which asymptotic optimality is shown. The finite sample performance is studied by simulations. Then we apply tests to real data sets.

Suggested Citation

  • Ostrovski, Vladimir, 2017. "Testing equivalence of multinomial distributions," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 77-82.
  • Handle: RePEc:eee:stapro:v:124:y:2017:i:c:p:77-82
    DOI: 10.1016/j.spl.2017.01.004
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    Cited by:

    1. Ostrovski, Vladimir, 2022. "Testing equivalence to binary generalized linear models with application to logistic regression," Statistics & Probability Letters, Elsevier, vol. 191(C).
    2. Luai Al-Labadi & Petru Ciur & Milutin Dimovic & Kyuson Lim, 2023. "Assessing Multinomial Distributions with a Bayesian Approach," Mathematics, MDPI, vol. 11(13), pages 1-16, July.
    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. Vladimir Ostrovski, 2019. "New Equivalence Tests for Approximate Independence in Contingency Tables," Stats, MDPI, vol. 2(2), pages 1-8, April.

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