Normalized random measures driven by increasing additive processes

• 成果类型: Article
• 署名作者: Nieto-Barajas, LE; Prünster, I; Walker, SG
• 署名单位: University of Pavia; University of Kent
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053604000000625
• 发表日期: 2004
• 页码: 2343-2360
• 关键词: dirichlet process; gaussian components; survival analysis; functionals; models; distributions; priors

摘要

This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes. In particular, we present results for the distribution of means under both prior and posterior conditions and, via the use of strategic latent variables, undertake a full Bayesian analysis. Our class of priors includes the well-known and widely used mixture of a Dirichlet process.