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    Bayesian density regression for discrete outcomes

    Papageorgiou, Georgios (2019) Bayesian density regression for discrete outcomes. Australian and New Zealand Journal of Statistics 61 (3), pp. 336-359. ISSN 1467-842X.

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    We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over- and/or under-dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilizing simulated and real datasets.


    Item Type: Article
    Additional Information: This is the peer reviewed version of the article, which has been published in final form at the link above. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Georgios Papageorgiou
    Date Deposited: 15 Aug 2019 05:15
    Last Modified: 09 Aug 2023 12:46


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