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Comparing distributions by using dependent normalized random‐measure mixtures
Author(s) -
Griffin J. E.,
Kolossiatis M.,
Steel M. F. J.
Publication year - 2013
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12002
Subject(s) - measure (data warehouse) , bayesian probability , inference , computer science , parametric statistics , sampling (signal processing) , superposition principle , statistical inference , bayesian inference , statistical physics , mathematics , statistics , data mining , artificial intelligence , physics , mathematical analysis , filter (signal processing) , computer vision
Summary 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.