A0236
Title: Linear models for multivariate repeated measures data
Authors: Anuradha Roy - The University of Texas at San Antonio (United States) [presenting]
Timothy Opheim - The University of Texas at San Antonio (United States)
Abstract: Multivariate repeated measures data, where observations are made on p response variables, and each response variable is measured over n sites or time points, construct matrix-variate response variables and arise across a wide range of disciplines, including medical, environmental and agricultural studies. The popularity of the classical general linear model (CGLM) is primarily due to the ease of modelling and authentication of the appropriateness of the model. However, CGLM is not appropriate and thus not applicable for multivariate data with multiple doubly correlated measurements. Extending the linear model for these doubly correlated multivariate data is proposed. Maximum likelihood estimates of the intercept and slope matrix parameters are derived. The practical implications of the methodological aspects of the proposed extended model for multivariate repeated measures data are demonstrated using two medical datasets.
