Bayesian nonparametric location–scale–shape mixtures

Antonio Canale () and Bruno Scarpa ()

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2016, vol. 25, issue 1, 113-130

Abstract: Discrete mixture models are one of the most successful approaches for density estimation. Under a Bayesian nonparametric framework, Dirichlet process location–scale mixture of Gaussian kernels is the golden standard, both having nice theoretical properties and computational tractability. In this paper we explore the use of the skew-normal kernel, which can naturally accommodate several degrees of skewness by the use of a third parameter. The choice of this kernel function allows us to formulate nonparametric location–scale–shape mixture prior with desirable theoretical properties and good performance in different applications. Efficient Gibbs sampling algorithms are also discussed and the performance of the methods are tested through simulations and applications to galaxy velocity and fertility data. Extensions to accommodate discrete data are also discussed. Copyright Sociedad de Estadística e Investigación Operativa 2016

Keywords: Discrete random probability measures; Model-based clustering; Skew-normal distribution; Rounded mixture priors; 62F15; 62E15 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11749-015-0446-2

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