A Bayesian analysis of generalized latent curve mixture models
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AbstractLatent curve models for longitudinal data have received increasing attention in medical, educational, psychological, and behavioral sciences. In these applied areas of research, heterogeneous longitudinal data are common. This paper proposes the use of generalized latent curve models for analyzing heterogenous longitudinal data. The basic model features a mixture of trajectories. It also employs a multinomial logit model for assessing the influence of fixed covariates and explanatory latent variables on the class membership probability within the mixture model. This broad class of models also handles non-normal data from the exponential family distributions. A Bayesian approach is implemented for data analysis. We report a simulation study that proves the satisfactory performance of the proposed approach. Furthermore, we analyzed a real data set extracted from the National Longitudinal Survey of Youth to illustrate the practical value of the proposed model and methodology.
All Author(s) ListPan JH, Song XY, Ip EH
Journal nameStatistics and Its Interface
Volume Number6
Issue Number1
PublisherInternational Press
Pages27 - 44
LanguagesEnglish-United Kingdom
KeywordsHeterogeneous longitudinal data; Latent curve mixture models; Markov chain Monte Carlo method; Modified deviance information criterion
Web of Science Subject CategoriesMathematical & Computational Biology; MATHEMATICAL & COMPUTATIONAL BIOLOGY; Mathematics; Mathematics, Interdisciplinary Applications; MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Last updated on 2021-18-09 at 00:21