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Title: Extending finite mixtures of t linear mixed-effects models with concomitant covariates Authors:  Yu-Chen Yang - National Chung Hsing University (Taiwan) [presenting]
Tsung-I Lin - National Chung Hsing University (Taiwan)
Luis Mauricio Castro - Pontificia Universidad Catolica de Chile (Chile)
Wan-Lun Wang - National Cheng Kung University (Taiwan)
Abstract: The issue of model-based clustering of longitudinal data has attracted increasing attention in the past two decades. Finite mixtures of Student's-$t$ linear mixed-effects (FM-tLME) models have been considered for implementing this task, especially when data contain extreme observations. An extended finite mixtures of Student's-t linear mixed-effects (EFM-tLME) model is presented, where the categorical component labels are assumed to be influenced by the observed covariates. Compared with the naive methods assuming the mixing proportions to be fixed but unknown, the proposed EFM-tLME model exploits a logistic function to link the relationship between the prior classification probabilities and the covariates of interest. To carry out maximum likelihood estimation, an alternating expectation conditional maximization (AECM) algorithm is developed under several model reduction schemes. The technique for extracting the information-based standard errors of parameter estimates is also investigated. The proposed method is illustrated using simulation experiments and real data from an AIDS clinical study.