Comparing distributions by using dependent normalized random-measure mixtures

• 成果类型: Article
• 署名作者: Griffin, J. E.; Kolossiatis, M.; Steel, M. F. J.
• 署名单位: University of Kent; Cyprus University of Technology; University of Warwick
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12002
• 发表日期: 2013
• 页码: 499-529
• 关键词: dirichlet process; bayesian-analysis; inference; models; priors

摘要

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.