Nonparametric estimation of large covariance matrices of longitudinal data
纵向数据大规模协方差矩阵的非参数估计
Estimation of an unstructured covariance matrix is difficult because of its positive‐definiteness constraint. This obstacle is removed by regressing each variable on its predecessors, so that estimation of a covariance matrix is shown to be equivalent to that of estimating a sequence of varying‐coefficient and varying‐order regression models. Our framework is similar to the use of increasing‐order autoregressive models in approximating the covariance matrix or the spectrum…