S3RI Seminar - Joint mean-covariance modelling and its R package: jmcm, by Jianxin Pan (Manchester) Seminar

Event details

Longitudinal studies commonly arise in various fields such as psychology, social science, economics and medical research, etc. It is of great importance to understand the dynamics in the mean function, covariance and/or correlation matrices of repeated measurements. However, high-dimensionality (HD) and positive-definiteness (PD) constraints are two major stumbling blocks in modelling of covariance and/or correlation matrices. It is evident that Cholesky-type decomposition based methods are effective in dealing with HD and PD problems, but those methods were not implemented in statistical software yet, making a difficulty for practitioners to use. In this talk, three Cholesky decomposition based methods for joint modelling of mean and covariance structures, namely Modified Cholesky decomposition (MCD), Alternative Cholesky decomposition (ACD) and Hyperpherical parameterization of Cholesky factor (HPC), will be introduced. The newly developed R package jmcm containing the MCD, ACD and HPC methods will then be introduced. Demonstration will be made by running the package jmcm and comparison of those methods will be made through analyzing real data examples. This is joint work with Yi Pan (The University of Manchester)