A0293
Title: Multivariate-t linear mixed models for multiple longitudinal data with censorship and fat-tailed behavior
Authors: Wan-Lun Wang - National Cheng Kung University (Taiwan) [presenting]
Tsung-I Lin - National Chung Hsing University (Taiwan)
Victor Hugo Lachos Davila - University of Connecticut (United States)
Abstract: The analysis of complex longitudinal data is challenging due to several inherent features: (i) more than one series of responses are repeatedly collected on each subject at irregularly occasions over a period of time; (ii) censorship due to limits of quantification of responses arises left- and/or right- censoring effects; (iii) outliers or heavy-tailed noises are possibly embodied within multiple response variables. The aim is to formulate the multivariate-t linear mixed model with censored responses (MtLMMC), which allows the analysts to model such data in the presence of the above described features simultaneously. An efficient expectation conditional maximization either (ECME) algorithm is developed to carry out maximum likelihood estimation of model parameters. The implementation of the E-step relies on the mean and covariance matrix of truncated multivariate-t distributions. The proposed methodology is illustrated through a simulation study and a real application on HIV/AIDS data.
