Title: Autoregressive skew-normal/independent linear mixed models
Authors: Victor Hugo Lachos Davila - University of Connecticut (United States) [presenting]
Abstract: In longitudinal studies, the repeated measures of each subject are collected over time and hence tend to be serially correlated. An extension of skew-normal/independent linear mixed models previously introduced is considered, where the error term has Ar(p) dependence. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated. We present an efficient EM-type algorithm for the computation of maximum likelihood estimation of parameters. The observed information matrix is derived analytically to account for standard errors, in addition, the technique for the prediction of future responses under this model is also investigated. The methodology is illustrated through an application to schizophrenia data and some simulation studies.