Parsimonious Covariance Matrix Estimation for Longitudinal Data
纵向数据的简约协方差矩阵估计
This article proposes a data-driven method to identify parsimony in the covariance matrix of longitudinal data and to exploit any such parsimony to produce a statistically efficient estimator of the covariance matrix. The approach parameterizes the covariance matrix through the Cholesky decomposition of its inverse. For longitudinal data, this is a one-step-ahead predictive representation, and the Cholesky factor is likely to have off-diagonal elements that are zero or close…