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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) (KAR id:63323)

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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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Fabrizio Leisen
Date Deposited: 08 Sep 2017 07:47 UTC
Last Modified: 10 Jan 2024 10:41 UTC
Resource URI: (The current URI for this page, for reference purposes)

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