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A Semiparametric and Location-Shift Copula-Based Mixture Model

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  • Gildas Mazo

    (Université catholique de Louvain)

Abstract

Modeling mixtures of distributions has rested on Gaussian distributions and/or a conditional independence hypothesis for a long time. Only recently have researchers begun to construct and study broader generic models without appealing to such hypotheses. Some of these extensions use copulas as a tool to build flexible models, as they permit modeling the dependence and the marginal distributions separately. But this approach also has drawbacks. First, the practitioner has to make more arbitrary choices, and second, marginal misspecification may loom on the horizon. This paper aims at overcoming these limitations by presenting a copulabased mixture model which is semiparametric. Thanks to a location-shift hypothesis, semiparametric estimation, also, is feasible, allowing for data adaptation without any modeling effort.

Suggested Citation

  • Gildas Mazo, 2017. "A Semiparametric and Location-Shift Copula-Based Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 444-464, October.
    • Handle: RePEc:spr:jclass:v:34:y:2017:i:3:d:10.1007_s00357-017-9243-9
    DOI: 10.1007/s00357-017-9243-9
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    References listed on IDEAS

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    1. Bordes, Laurent & Chauveau, Didier & Vandekerkhove, Pierre, 2007. "A stochastic EM algorithm for a semiparametric mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5429-5443, July.
    2. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2015. "Model-Based Clustering for Conditionally Correlated Categorical Data," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 145-175, July.
    3. M. Levine & D. R. Hunter & D. Chauveau, 2011. "Maximum smoothed likelihood for multivariate mixtures," Biometrika, Biometrika Trust, vol. 98(2), pages 403-416.
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    Cited by:

    1. Christophe Biernacki & Matthieu Marbac & Vincent Vandewalle, 2021. "Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 129-157, April.
    2. Mazo, Gildas & Averyanov, Yaroslav, 2019. "Constraining kernel estimators in semiparametric copula mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 170-189.
    3. Douglas L. Steinley, 2018. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 35(2), pages 195-197, July.

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