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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.
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页码:4449 / 4463
页数:15
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