Low information omnibus (LIO) priors for Dirichlet process mixture models

DOI10.1214/18-BA1119zbMath1421.62078OpenAlexW2893855365MaRDI QIDQ2316979

Anjishnu Banerjee, Yushu Shi, Michael J. Martens, Purushottam W. Laud

Publication date: 7 August 2019

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1560240023

zbMATH Keywords

survival analysisdensity estimationDirichlet process mixture modelBayesian nonparametric methodslow-information prior

Mathematics Subject Classification ID

Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Reliability and life testing (62N05)

Related Items

A dependent Dirichlet process model for survival data with competing risks, A Robustified Posterior for Bayesian Inference on a Large Number of Parallel Effects, On the inferential implications of decreasing weight structures in mixture models, Dirichlet process mixtures under affine transformations of the data, A review of uncertainty quantification for density estimation, A Dirichlet process mixture model for non-ignorable dropout, Low information omnibus (LIO) priors for Dirichlet process mixture models

Uses Software

• DPpackage
• BayesDA

Cites Work

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