Jianxin Pan (University of Manchester)

Joint modelling of mean and covariance structures for longitudinal data

When analysing longitudinal/correlated data, misspecification of covariance structures may lead to very inefficient estimators of parameters in the mean. In some circumstances, e.g., when missing data are present, it may result in biased estimators of the mean parameters. Hence, correct models for covariance structures play a very important role. Like the mean, covariance structures can be actually modelled using linear or nonlinear regression model techniques. A number of estimation methods were developed recently for modelling of mean and covariance structures, simultaneously. In this talk, I will review some methods on joint modelling of the mean and covariance structures for longitudinal data, including linear, non-linear regression models and semiparametric models. Real examples and simulation studies will be provided for illustration.