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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Official URL: http://dx.doi.org/10.1214/17-BA1072 |
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 |
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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: | https://kar.kent.ac.uk/id/eprint/63323 (The current URI for this page, for reference purposes) |
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