Consistent estimation of mixture complexity

• 成果类型: Article
• 署名作者: James, LF; Preibe, CE; Marchette, DJ
• 署名单位: Johns Hopkins University; United States Department of Defense; United States Navy
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 2001
• 页码: 1281-1296
• 关键词: hellinger distance estimation; bayesian density-estimation; maximum-likelihood; finite mixture; location; kernel; ORDER

摘要

The consistent estimation of mixture complexity is of fundamental importance in many applications of finite mixture models. An enormous body of literature exists regarding the application, computational issues and theoretical aspects of mixture models when the number of components is known, but estimating the unknown number of components remains an area of intense research effort. This article presents a semiparametric methodology yielding almost sure convergence of the estimated number of components to the true but unknown number of components. The scope of application is vast, as mixture models are routinely employed across the entire diverse application range of statistics, including nearly all of the social and experimental sciences.