Longitudinal analysis of repeated binary data using autoregressive and random effect modelling

作     者：Aitkin, M Alfò, M 

作者机构：Univ Roma La Sapienza Probabil & Stat Appl Dipartimento Stat I-00185 Rome Italy Univ Newcastle Upon Tyne Sch Math & Stat Newcastle Upon Tyne NE1 7RU Tyne & Wear England Educ Stat Serv Inst Washington DC USA 

出 版 物：《STATISTICAL MODELLING》 (Stat. Model.)

年 卷 期：2003年第3卷第4期

页      面：291-303页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：autoregressive models nonparametric maximum likelihood (NPML) estimation random effects GLMs repeated binary data 

摘      要：In this paper we extend random coefficient models for binary repeated responses to include serial dependence of Markovian form, with the aim of defining a general association structure among responses recorded on the same individual. We do not adopt a parametric specification for the random coefficients distribution and this allows us to overcome inconsistencies due to misspecification of this component. Model parameters are estimated by means of an EM algorithm for nonparametric maximum likelihood (NPML), which is extended to deal with serial correlation among repeated measures, with an explicit focus on those situations where short individual time series have been observed. The approach is described by presenting a reanalysis of the well-known Muscatine (Iowa) longitudinal study on childhood obesity.