• Bernoulli
  • Volume 23, Number 1 (2017), 379-404.

Posterior asymptotics of nonparametric location-scale mixtures for multivariate density estimation

Antonio Canale and Pierpaolo De Blasi

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Density estimation represents one of the most successful applications of Bayesian nonparametrics. In particular, Dirichlet process mixtures of normals are the gold standard for density estimation and their asymptotic properties have been studied extensively, especially in the univariate case. However, a gap between practitioners and the current theoretical literature is present. So far, posterior asymptotic results in the multivariate case are available only for location mixtures of Gaussian kernels with independent prior on the common covariance matrix, while in practice as well as from a conceptual point of view a location-scale mixture is often preferable. In this paper, we address posterior consistency for such general mixture models by adapting a convergence rate result which combines the usual low-entropy, high-mass sieve approach with a suitable summability condition. Specifically, we establish consistency for Dirichlet process mixtures of Gaussian kernels with various prior specifications on the covariance matrix. Posterior convergence rates are also discussed.

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Bernoulli, Volume 23, Number 1 (2017), 379-404.

Received: July 2014
Revised: June 2015
First available in Project Euclid: 27 September 2016

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Bayesian nonparametrics density estimation Dirichlet mixture factor model posterior asymptotics sparse random eigenmatrices


Canale, Antonio; De Blasi, Pierpaolo. Posterior asymptotics of nonparametric location-scale mixtures for multivariate density estimation. Bernoulli 23 (2017), no. 1, 379--404. doi:10.3150/15-BEJ746.

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