Non-parametric estimation of finite mixtures from repeated measurements

被引：29

作者：

Bonhomme, Stephane ^{[1
]}

Jochmans, Koen ^{[2
]}

Robin, Jean-Marc ^{[2
,3
]}

机构：

[1] Univ Chicago, Chicago, IL 60637 USA

[2] Sci Po, F-75007 Paris, France

[3] UCL, London WC1E 6BT, England

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY | 2016年 / 78卷 / 01期

基金：

英国经济与社会研究理事会; 欧洲研究理事会;

关键词：

Finite mixture; Repeated measurement data; Reweighting; Two-step estimation; IDENTIFICATION; INFERENCE; MODELS; LIKELIHOOD;

D O I：

10.1111/rssb.12110

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：211 / 229

页数：19

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