Additive mixed models with approximate Dirichlet process mixtures: the EM approach

作     者：Heinzl, Felix Tutz, Gerhard 

作者机构：Akad Str 1 D-80799 Munich Germany 

出 版 物：《STATISTICS AND COMPUTING》 (Stat. Comput.)

年 卷 期：2016年第26卷第1-2期

页      面：73-92页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Deutsche Forschungsgemeinschaft  DFG  (208398175) 

主　　题：Additive mixed models Dirichlet process mixture EM algorithm Penalized splines Stick breaking 

摘      要：We consider additive mixed models for longitudinal data with a nonlinear time trend. As random effects distribution an approximate Dirichlet process mixture is proposed that is based on the truncated version of the stick breaking presentation of the Dirichlet process and provides a Gaussian mixture with a data driven choice of the number of mixture components. The main advantage of the specification is its ability to identify clusters of subjects with a similar random effects structure. For the estimation of the trend curve the mixed model representation of penalized splines is used. An Expectation-Maximization algorithm is given that solves the estimation problem and that exhibits advantages over Markov chain Monte Carlo approaches, which are typically used when modeling with Dirichlet processes. The method is evaluated in a simulation study and applied to theophylline data and to body mass index profiles of children.