Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets
نویسندگان
چکیده
This paper generalizes the Sparse Precision matrix Selection (SPS) algorithm, proposed by Davanloo et al. (2015) for estimating scalar Gaussian Random Field (GRF) models, to the multivariate, second-order stationary case under a separable covariance function. Theoretical convergence rates for the estimated covariance matrix and for the estimated parameters of the correlation function are established. Numerical simulation results validate our theoretical findings. Data segmentation is used to handle large data sets.
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