PARAMETRIC MODELS FOR AN - SPLITTING PROCESSES AND MIXTURES

• 成果类型: Article
• 署名作者: HILL, BM
• 署名单位: University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• 发表日期: 1993
• 页码: 423-433
• 关键词: conglomerability

摘要

A class of parametric models, called splitting processes, is defined, by using de Finetti's concept of adherent mass. Such splitting processes give rise to complex mixtures of distributions. It is proved that the nonparametric Bayesian predictive procedure A(n), of Hill, holds exactly for a member of this class called a nested splitting process. The connection between A(n) and the Dirichlet process is stated and proved. A multivariate version of A(n), based on splitting processes, is proposed.