Covariance modelling: recent developments Seminar

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 very biased estimators of the mean parameters. Hence, correct models for covariance structures play a very important role in statistical inferences. Like the mean, covariance structures can be modelled using linear or nonlinear regression model strategy. Various estimation methods were proposed recently to model the mean and covariance structures, simultaneously. In this talk, I will review those methods on joint modelling of the mean and covariance structures for longitudinal data, including linear, nonparametric regression models and semiparametric models. If time is allowed, missing data and variable selection work will be presented too. Real examples and simulation studies will be given for illustration.