A Comparison of Estimation Methods For Nonlinear Mixed Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study

被引：0
|
作者：
Harring, Jeffrey R. [1 ]
Liu, Junhui [2 ]
机构：
[1] Univ Maryland, Dept Human Dev & Quantitat Methodol, Measurement Stat & Evaluat, College Pk, MD 20742 USA
[2] Educ Testing Serv, Princeton, NJ 08541 USA
来源：
JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2016年 / 15卷 / 01期
关键词：
Random coefficient models; linearization; quadrature; Bayesian; nonlinear models; non-normality;
D O I：
暂无
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：
A Monte Carlo simulation is employed to investigate the performance of five estimation methods of nonlinear mixed effects models in terms of parameter recovery and efficiency of both regression coefficients and variance/covariance parameters under varying levels of data sparseness and model misspecification.
引用
收藏
页码：539 / 569
页数：31
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