A smooth nonparametric estimate of a mixing distribution using mixtures of Gaussians

• 成果类型: Article
• 署名作者: Magder, LS; Zeger, SL
• 署名单位: Johns Hopkins University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2291733
• 发表日期: 1996
• 页码: 1141-1151
• 关键词: maximum-likelihood-estimation; mixed-effects models; seroconverters; algorithms; inference

摘要

We propose a method of estimating mixing distributions using maximum likelihood over the class of arbitrary mixtures of Gaussians subject to the constraint that the component variances be greater than or equal to some minimum value h. This approach can lead to estimates of many shapes, with smoothness controlled by parameter h. We show that the resulting estimate will always be a finite mixture of Gaussians, each having variance h. The nonparametric maximum likelihood estimate can be viewed as a special case, with h = 0. The method can be extended to estimate multivariate mixing distributions. Examples and the results of a simulation study are presented.