Comparing distributions by using dependent normalized random-measure mixtures

Griffin, Jim E. and Kolossiatis, Michalis and Steel, Mark F.J. (2013) Comparing distributions by using dependent normalized random-measure mixtures. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75 (3). pp. 499-529. ISSN 1369-7412. (doi:https://doi.org/10.1111/rssb.12002) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1111/rssb.12002

Abstract

A methodology for the simultaneous Bayesian nonparametric modelling of several distributions is developed. Our approach uses normalized random measures with independent increments and builds dependence through the superposition of shared processes. The properties of the prior are described and the modelling possibilities of this framework are explored in some detail. Efficient slice sampling methods are developed for inference. Various posterior summaries are introduced which allow better understanding of the differences between distributions. The methods are illustrated on simulated data and examples from survival analysis and stochastic frontier analysis.

Item Type: Article
Uncontrolled keywords: Bayesian nonparametrics; Dependent distributions; Dirichlet process; Normalized Generalized Gamma process; Slice sampling; Utility function
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Michalis Kolossiatis
Date Deposited: 06 Nov 2013 18:56 UTC
Last Modified: 29 Apr 2014 15:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/36080 (The current URI for this page, for reference purposes)
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