KCYP data analysis using Bayesian multivariate linear model

Although longitudinal studies mainly produce multivariate longitudinal data, most of existing statistical mod-els analyze univariate longitudinal data and there is a limitation to explain complex correlations properly. There-fore, this paper describes various methods of modeling the covariance matrix to explain the complex correlations. Among them, modified Cholesky decomposition, modified Cholesky block decomposition, and hypersphere de-composition are reviewed. In this paper, we review these methods and analyze Korean children and youth panel (KCYP) data are analyzed using the Bayesian method. The KCYP data are multivariate longitudinal data that have response variables: School adaptation, academic achievement, and dependence on mobile phones. Assum-ing that the correlation structure and the innovation standard deviation structure are different, several models are compared. For the most suitable model, all explanatory variables are significant for school adaptation, and aca-demic achievement and only household income appears as insignificant variables when cell phone dependence is a response variable.