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Bayesian nonparametric inference for unimodal skew-symmetric distributions

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  • A. Ghalamfarsa Mostofi
  • M. Kharrati-Kopaei

Abstract

This paper studies the case where the observations come from a unimodal and skew density function with an unknown mode. The skew-symmetric representation of such a density has a symmetric component which can be written as a scale mixture of uniform densities. A Dirichlet process (DP) prior is assigned to mixing distribution. We also assume prior distributions for the mode and the skewed component. A computational approach is used to obtain the Bayes estimate of the components. An example is given to illustrate the approach. Copyright Springer-Verlag 2012

Suggested Citation

  • A. Ghalamfarsa Mostofi & M. Kharrati-Kopaei, 2012. "Bayesian nonparametric inference for unimodal skew-symmetric distributions," Statistical Papers, Springer, vol. 53(4), pages 821-832, November.
    • Handle: RePEc:spr:stpapr:v:53:y:2012:i:4:p:821-832
    DOI: 10.1007/s00362-011-0385-2
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    References listed on IDEAS

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    Cited by:

    1. Teimouri, Mahdi & Nadarajah, Saralees, 2013. "On simulating Balakrishnan skew-normal variates," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 52-58.
    2. Haydar Demirhan & Kamil Demirhan, 2016. "A Bayesian approach for the estimation of probability distributions under finite sample space," Statistical Papers, Springer, vol. 57(3), pages 589-603, September.
    3. Jianjun Zhang & Lei Yang & Xianyi Wu, 2019. "Polya tree priors and their estimation with multi-group data," Statistical Papers, Springer, vol. 60(3), pages 849-875, June.

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