Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9853
Title: Comparing distributions by using dependent normalized random-measure mixtures
Authors: Griffin, Jim E. 
Kolossiatis, Michalis 
Steel, Mark F J 
Major Field of Science: Natural Sciences
Field Category: Mathematics
Keywords: Bayesian non-parametrics;Dependent distributions;Dirichlet process;Normalized generalized gamma process;Slice sampling;Utility function
Issue Date: 7-Feb-2013
Source: Journal of the Royal Statistical Society, Series B: Statistical Methodology, 2013, vol. 75, no. 3, pp. 499-529
Volume: 75
Issue: 3
Start page: 499
End page: 529
Journal: Journal of the Royal Statistical Society, Series B: Statistical Methodology 
Abstract: A methodology for the simultaneous Bayesian non-parametric 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 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.
URI: https://hdl.handle.net/20.500.14279/9853
ISSN: 13697412
DOI: 10.1111/rssb.12002
Rights: © Royal Statistical Society.
Type: Article
Affiliation : University of Kent at Canterbury 
Cyprus University of Technology 
University of Warwick 
Publication Type: Peer Reviewed
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