Statistical Science

Modeling with Normalized Random Measure Mixture Models

Ernesto Barrios, Antonio Lijoi, Luis E. Nieto-Barajas, and Igor Prünster

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The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimation and clustering. The goal of this paper is to illustrate the use of normalized random measures as mixing measures in nonparametric hierarchical mixture models and point out how possible computational issues can be successfully addressed. To this end, we first provide a concise and accessible introduction to normalized random measures with independent increments. Then, we explain in detail a particular way of sampling from the posterior using the Ferguson–Klass representation. We develop a thorough comparative analysis for location-scale mixtures that considers a set of alternatives for the mixture kernel and for the nonparametric component. Simulation results indicate that normalized random measure mixtures potentially represent a valid default choice for density estimation problems. As a byproduct of this study an R package to fit these models was produced and is available in the Comprehensive R Archive Network (CRAN).

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Statist. Sci., Volume 28, Number 3 (2013), 313-334.

First available in Project Euclid: 28 August 2013

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Bayesian nonparametrics completely random measure clustering density estimation Dirichlet process increasing additive process latent variables mixture model normalized generalized gamma process normalized inverse Gaussian process normalized random measure normalized stable process


Barrios, Ernesto; Lijoi, Antonio; Nieto-Barajas, Luis E.; Prünster, Igor. Modeling with Normalized Random Measure Mixture Models. Statist. Sci. 28 (2013), no. 3, 313--334. doi:10.1214/13-STS416.

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