Modeling of Covariance Structure for Continuous and Discrete Dependent Data

This session includes several talks on modeling of covariance structure for continuous and/or discrete dependent data. Firstly, deep leaning method is applied to modelling of covariance structure for longitudinal data and its advantage over the existing linear or nonlinear model assumption-based methods are presented. Secondly, modeling of covariance structure for spatially discrete data, particularly spatially binary data, is provided through using Copula approach. Thirdly, modeling of covariance structure of mixed longitudinal data, including continuous, binary and ordered longitudinal data, is addressed. The proposed methods are applied to real data examples arising in practices.

Organized by:

Jianxin Pan (China)