Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm
Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm
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DOI：
10.1016/j.csda.2013.05.026
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
L. Fu;Mingliang Wang;Y. Lei;Shouzheng Tang
中科院分区：
文献类型：
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作者：
L. Fu;Mingliang Wang;Y. Lei;Shouzheng Tang
多水平非线性混合效应（ML-NLME）模型因其在处理多学科重复测量数据方面的灵活性，近年来受到了广泛的关注。在这项研究中，我们提出了两个水平的随机效应的ML-NLME模型的最大似然和限制最大似然估计，使用一阶条件展开（FOCE）和期望最大化（EM）算法。将FOCE-EM算法与最流行的Lindstrom和Bates（LB）方法在计算和统计特性方面进行了比较。利用杉木试验林断面积生长系列实测数据和模拟数据进行评价。FOCE-EM和LB算法给出了相同的参数估计值和拟合统计量，用于由两者收敛的模型。然而，FOCE-EM收敛的所有模型，而LB没有，特别是对于两个水平的随机效应，同时考虑在几个基本参数，以解释组间变异的模型。我们建议在ML-NLME模型中使用FOCE-EM，特别是当收敛是模型选择中的一个问题时。
Multi-level nonlinear mixed effects (ML-NLME) models have received a great deal of attention in recent years because of the flexibility they offer in handling the repeated-measures data arising from various disciplines. In this study, we propose both maximum likelihood and restricted maximum likelihood estimations of ML-NLME models with two-level random effects, using first order conditional expansion (FOCE) and the expectation–maximization (EM) algorithm. The FOCE–EM algorithm was compared with the most popular Lindstrom and Bates (LB) method in terms of computational and statistical properties. Basal area growth series data measured from Chinese fir (Cunninghamia lanceolata) experimental stands and simulated data were used for evaluation. The FOCE–EM and LB algorithms given the same parameter estimates and fit statistics for models that converged by both. However, FOCE–EM converged for all the models, while LB did not, especially for the models in which two-level random effects are simultaneously considered in several base parameters to account for between-group variation. We recommend the use of FOCE–EM in ML-NLME models, particularly when convergence is a concern in model selection.