文献类型：期刊文献

英文题名：Parameter estimation of two-level nonlinear mixed effects models using first order conditional linearization and the EM algorithm

作者：Fu, Liyong[1] Wang, Mingliang[2] Lei, Yuancai[1] Tang, Shouzheng[1]

第一作者：符利勇

通信作者：Tang, SZ[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Univ Georgia, Warnell Sch Forestry & Nat Resources, Athens, GA 30602 USA

年份：2014

卷号：69

起止页码：173-183

外文期刊名：COMPUTATIONAL STATISTICS & DATA ANALYSIS

收录：;EI(收录号：20133716724392);Scopus(收录号：2-s2.0-84883349978);WOS:【SCI-EXPANDED(收录号:WOS:000326766600014)】；

基金：The authors express their appreciation to the Chinese National Natural Science Foundations (NOS. 31270679, 31300534, 31170588) for financial support for this study, and the Research Institute of Forestry of the Chinese Academy of Forestry for providing the Chinese fir data.

语种：英文

外文关键词：Cunninghamia lanceolata; Expectation-maximization algorithm; First order conditional expansion; Lindstrom and Bates algorithm; Simulated data; Two-level nonlinear mixed effects models

摘要：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. (C) 2013 Elsevier B.V. All rights reserved.