Extending multivariate-t linear mixed models for multiple longitudinal data with censored responses and heavy tails

Autor(es)

Wang, Wan-Lun ^[1] ; Lin, Tsung-I ^{[2, 3]} ; Lachos, Victor H. ^[4]

Afiliação do(s) autor(es)

^[1] Feng Chia Univ, Dept Stat, Grad Inst Stat & Actuarial Sci, Taichung - Taiwan

^[2] Natl Chung Hsing Univ, Inst Stat, Taichung - Taiwan

^[3] China Med Univ, Dept Publ Hlth, Taichung - Taiwan

^[4] Univ Estadual Campinas, Dept Estat, Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: STATISTICAL METHODS IN MEDICAL RESEARCH; v. 27, n. 1, p. 48-64, JAN 2018.
• Citações Web of Science: 11

Resumo

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. This article formulates 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. To enhance the computational efficiency, two auxiliary permutation matrices are incorporated into the procedure to determine the observed and censored parts of each subject. The proposed methodology is demonstrated via a simulation study and a real application on HIV/AIDS data. (AU)

• Processo FAPESP: 14/02938-9 - Estimação e diagnóstico em modelos de efeitos mistos para dados censurados usando misturas de escala skew-normal
• Beneficiário: Víctor Hugo Lachos Dávila
• Modalidade de apoio: Auxílio à Pesquisa - Regular