title: Selection of working correlation structure in generalized estimating equations.
abstract: The well-known generalized estimating equations is a very popular approach for analyzing longitudinal data. Selecting an appropriate correlation structure in the generalized estimating equations framework is a key step for estimating parameters efficiently and deriving reliable statistical inferences. We present two new criteria for selecting the best among the candidates with any arbitrary structures, even for irregularly timed measurements. The simulation results demonstrate that the new criteria perform more similarly to EAIC and EBIC as the sample size becomes large. However, their performance is much enhanced when the sample size is small and the number of measurements is large. Finally, three real datasets are used to illustrate the proposed criteria. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology, Australia.
authors.forename: You-Gan
authors.initials: YG
authors.lastname: Wang
authors.affiliation: School of Mathematics and Statistics, Xi'an Jiaotong University, China.
authors.forename: Liya
authors.initials: L
authors.lastname: Fu
mesh_terms: D000293:Adolescent; D056808:Biostatistics; D002453:Cell Cycle / Q000235:genetics; D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D005533:Foot Dermatoses / Q000188:drug therapy; D015966:Gene Expression Regulation, Fungal; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D014009:Onychomycosis / Q000188:drug therapy; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics; D018401:Sample Size; D014070:Tooth / Q000254:growth & development
keywords: empirical likelihood; longitudinal data; model selection; working correlation matrix