Joint Estimation of Multiple Graphical Models from High Dimensional Time Series
高维时间序列中多个图模型的联合估计
Summary We consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel-based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework,…