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Modelling and computation using NCoRM mixtures for density regression

Griffin, Jim E., Leisen, Fabrizio (2017) Modelling and computation using NCoRM mixtures for density regression. Bayesian Analysis, 13 (3). pp. 897-916. ISSN 1936-0975. (doi:10.1214/17-BA1072)

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Abstract

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized compound random measure mixture models. The algorithm makes use of a new general approach to the unbiased estimation of Laplace functionals of compound random measures (which includes completely random measures as a special case). The approach is illustrated on problems of density regression.

Item Type: Article
DOI/Identification number: 10.1214/17-BA1072
Uncontrolled keywords: dependent random measures; mixture models; multivariate Lévy measures; pseudo-marginal samplers; Poisson estimator
Subjects: H Social Sciences > HA Statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Fabrizio Leisen
Date Deposited: 08 Sep 2017 07:47 UTC
Last Modified: 01 Aug 2019 10:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63323 (The current URI for this page, for reference purposes)
Griffin, Jim E.: https://orcid.org/0000-0002-4828-7368
Leisen, Fabrizio: https://orcid.org/0000-0002-2460-6176
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