文章基本信息

标题：A Comparison of Estimation Methods for Nonlinear Mixed-Effects Models Under Model Misspecification and Data Sparseness: A Simulation Study
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作者：Harring, Jeffrey R. ; Liu, Junhui
期刊名称：Journal of Modern Applied Statistical Methods
出版年度：2016
卷号：15
期号：1
页码：27
出版社：Wayne State University
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.
关键词：Random coefficient models; linearization; quadrature; Bayesian; nonlinear models; non-normality