Non-parametric estimation of finite mixtures from repeated measurements

• 成果类型: Article
• 署名作者: Bonhomme, Stephane; Jochmans, Koen; Robin, Jean-Marc
• 署名单位: University of Chicago; Institut d'Etudes Politiques Paris (Sciences Po); University of London; University College London
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12110
• 发表日期: 2016
• 页码: 211-229
• 关键词: identification; inference; models; likelihood

摘要

This paper provides methods to estimate finite mixtures from data with repeated measurements non-parametrically. We present a constructive identification argument and use it to develop simple two-step estimators of the component distributions and all their functionals. We discuss a computationally efficient method for estimation and derive asymptotic theory. Simulation experiments suggest that our theory provides confidence intervals with good coverage in small samples.