Bayesian nonparametric inference on stochastic ordering

Biometrika
David B Dunson, Shyamal D Peddada

Abstract

This article considers Bayesian inference about collections of unknown distributions subject to a partial stochastic ordering. To address problems in testing of equalities between groups and estimation of group-specific distributions, we propose classes of restricted dependent Dirichlet process priors. These priors have full support in the space of stochastically ordered distributions, and can be used for collections of unknown mixture distributions to obtain a flexible class of mixture models. Theoretical properties are discussed, efficient methods are developed for posterior computation using Markov chain Monte Carlo, and the methods are illustrated using data from a study of DNA damage and repair.

References

Aug 21, 2003·Biometrics·David B Dunson, Brian Neelon
Feb 21, 2006·Biostatistics·David B Dunson
Dec 13, 2006·Biometrics·Michael L Pennell, David B Dunson

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Citations

Feb 13, 2010·Biostatistics·Björn BornkampDavid Dunson
Apr 17, 2012·Journal of the American Statistical Association·Hongxia YangDavid B Dunson
Jul 24, 2012·Biometrika·Lianming Wang, David B Dunson
Apr 6, 2016·Biometrika·Eric F Lock, David B Dunson
Dec 1, 2009·Journal of the American Statistical Association·Yeonseung Chung, David B Dunson
Feb 1, 2011·Annals of the Institute of Statistical Mathematics·Yeonseung Chung, David B Dunson

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