文献类型：期刊文献

英文题名：Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming

作者：Fu, Liyong[1,2] Wang, Mingliang[3] Wang, Zuoheng[4] Song, Xinyu[5] Tang, Shouzheng[1]

第一作者：Fu, Liyong;符利勇

通信作者：Fu, LY[1];Fu, LY[2]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Penn State Univ, Ctr Stat Genet, Loc T3436,Mailcode CH69,500 Univ Dr, Hershey, PA 17033 USA;[3]Univ Georgia, Warnell Sch Forestry & Nat Resources, Athens, GA 30602 USA;[4]Yale Sch Publ Hlth, Dept Biostat, New Haven, CT 06520 USA;[5]Xinyang Normal Univ, Coll Comp & Informat Tech, Xinyang 464000, Henan, Peoples R China

年份：2019

卷号：12

期号：5

外文期刊名：INTERNATIONAL JOURNAL OF BIOMATHEMATICS

收录：;Scopus(收录号：2-s2.0-85068422236);WOS:【SCI-EXPANDED(收录号:WOS:000480300100001)】；

基金：The authors would like to thank the Thirteenth Five-year Plan Pioneering project of High Technology Plan of the National Department of Technology (No. 2017YFC0504101) and the National Natural Science Foundations of China (Nos. 31470641, 31300534 and 31570628) for the financial support of this study.

语种：英文

外文关键词：Crossed random effects; first-order conditional expansion; nested random effects; nonlinear mixed-effects models; sequential quadratic programming

摘要：Nonlinear mixed-effects (NLME) models have become popular in various disciplines over the past several decades. However, the existing methods for parameter estimation implemented in standard statistical packages such as SAS and R/S-Plus are generally limited to single- or multi-level NLME models that only allow nested random effects and are unable to cope with crossed random effects within the framework of NLME modeling. In this study, we propose a general formulation of NLME models that can accommodate both nested and crossed random effects, and then develop a computational algorithm for parameter estimation based on normal assumptions. The maximum likelihood estimation is carried out using the first-order conditional expansion (FOCE) for NLME model linearization and sequential quadratic programming (SQP) for computational optimization while ensuring positive-definiteness of the estimated variance-covariance matrices of both random effects and error terms. The FOCE-SQP algorithm is evaluated using the height and diameter data measured on trees from Korean larch (L. olyensis var. Chang-paiensis) experimental plots as well as simulation studies. We show that the FOCE-SQP method converges fast with high accuracy. Applications of the general formulation of NLME models are illustrated with an analysis of the Korean larch data.