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Non parametric mixture priors based on an exponential random scheme

Author

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  • Sonia Petrone

    (Università L. Bocconi)

  • Piero Veronese

    (Università L. Bocconi)

Abstract

We propose a general procedure for constructing nonparametric priors for Bayesian inference. Under very general assumptions, the proposed prior selects absolutely continuous distribution functions, hence it can be useful with continuous data. We use the notion ofFeller-type approximation, with a random scheme based on the natural exponential family, in order to construct a large class of distribution functions. We show how one can assign a probability to such a class and discuss the main properties of the proposed prior, namedFeller prior. Feller priors are related to mixture models with unknown number of components or, more generally, to mixtures with unknown weight distribution. Two illustrations relative to the estimation of a density and of a mixing distribution are carried out with respect to well known data-set in order to evaluate the performance of our procedure. Computations are performed using a modified version of an MCMC algorithm which is briefly described.

Suggested Citation

  • Sonia Petrone & Piero Veronese, 2002. "Non parametric mixture priors based on an exponential random scheme," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(1), pages 1-20, February.
    • Handle: RePEc:spr:stmapp:v:11:y:2002:i:1:d:10.1007_bf02511443
    DOI: 10.1007/BF02511443
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    References listed on IDEAS

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    1. Sonia Petrone, 1999. "Random Bernstein Polynomials," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(3), pages 373-393, September.
    2. Sonia Petrone & Larry Wasserman, 2002. "Consistency of Bernstein polynomial posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(1), pages 79-100, January.
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    Cited by:

    1. Sandra Fortini & Sonia Petrone, 2020. "Quasi‐Bayes properties of a procedure for sequential learning in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1087-1114, September.
    2. repec:dau:papers:123456789/3984 is not listed on IDEAS
    3. Xiang Zhang & Yanbing Zheng, 2014. "Nonparametric Bayesian inference for multivariate density functions using Feller priors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(2), pages 321-340, June.

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