ARMA Cholesky Factor Models for the Covariance Matrix of Linear Models.

ABSTRACT: In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcomes these limitations, two Cholesky decomposition approaches have been proposed: modified Cholesky decomposition for autoregressive (AR) structure and moving average Cholesky decomposition for moving average (MA) structure, respectively. However, the correlations of repeated outcomes are often not captured parsimoniously using either approach separately. In this paper, we propose a class of flexible, nonstationa

SUBMITTER: Lee K 

PROVIDER: S-EPMC5669060 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature