Profile maximal likelihood estimation for non linear mixed models with longitudinal data

被引:0
|
作者
Li, Zaixing [1 ,2 ]
机构
[1] China Univ Min & Technol, Dept Math, Beijing, Peoples R China
[2] China Univ Min & Technol, State Key Lab Coal Resource & Safe Min, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Asymptotic properties; EM algorithm; NLMM; PMLE; LAPLACES APPROXIMATION; EM-ALGORITHM;
D O I
10.1080/03610926.2015.1085561
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
In this article, the profile maximal likelihood estimate (PMLE) is proposed for non linear mixed models (NLMMs) with longitudinal data where the variance components are estimated by the expectation-maximization (EM) algorithm. Strong consistency and the asymptotic normality of the estimators are derived. A simulation study is conducted where the performance of the PLME and the Fishing scoring estimate (FSE) in literatures are compared. Moreover, a real data is also analyzed to investigate the empirical performance of the procedure.
引用
收藏
页码:4449 / 4463
页数:15
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