Modeling Large Nonstationary Spatial Data with the Full-Scale Basis Graphical Lasso
全尺度基图套索:大尺度非平稳空间数据建模
We propose a new approach for the modeling large datasets of nonstationary spatial processes that combines a latent low rank process and a sparse covariance model. The low rank component coefficients are endowed with a flexible graphical Gaussian Markov random field model. The utilization of a low rank and compactly-supported covariance structure combines the full-scale approximation and the basis graphical lasso; we term this new approach the full-scale basis graphical…