Title: Covariance regression analysis
Abstract: I will introduce covariance regression analysis which is proposed recently. This method mainly takes the auxiliary information matrix as the covariates. Then for a p-dimensional response vector, the covariance matrix is modeled by the linear combination of the auxiliary information matrix. I will share the basic ideas of the covariance regression model, furthermore, three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators will be introduced. Finally, an example will illustrate the comparison of these three types of estimators.