Scale mixture of skew‐normal linear mixed models with within‐subject serial dependence. (12th January 2021)

• Record Type: Journal Article
• Authors: Schumacher, Fernanda L.; Lachos, Victor H.; Matos, Larissa A.
• Is Part Of: Statistics in medicine. Volume 40:Number 7(2021)
• Page Start: 1790
• Page End: 1810
• Publication Date: 2021-01-12
• Subjects: autoregressive AR(p) -- damped exponential correlation -- EM‐algorithm -- irregularly observed longitudinal data -- linear mixed models -- scale mixtures of skew‐normal distributions; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8870
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. These studies are commonly analyzed using linear mixed models (LMMs), and in this article we consider an extension of the skew‐normal/independent LMM, where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p . The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated. For this robust model, we present an efficient EM‐type algorithm for parameters estimation via maximum likelihood and the observed information matrix is derived analytically to account for standard errors. The methodology is illustrated through an application to schizophrenia data and some simulation studies. The proposed algorithm and methods are implemented in the new R package skewlmm .