Estimating a unimodal distribution from interval-censored data

• 成果类型: Article
• 署名作者: Duembgen, Lutz; Freitag-Wolf, Sandra; Jongbloed, Geurt
• 署名单位: University of Bern; University of Kiel; Vrije Universiteit Amsterdam
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000000032
• 发表日期: 2006
• 页码: 1094-1106
• 关键词: Consistency

摘要

In this article we consider three nonparametric maximum likelihood estimators based on mixed-case interval-censored data. Apart from the unrestricted estimator, we consider estimators under the assumption that the underlying distribution function of event times is concave or unimodal. Characterizations of the estimates are derived, and algorithms are proposed for their computation. The estimators are shown to be asymptotically consistent, and the benefits of additional constraints are illustrated through simulations. Finally, the estimators are used as an ingredient in a nonparametric comparison of two samples.