Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets
Machine Learning
2021-01-12 v2
Abstract
We present a new method for estimating multivariate, second-order stationary Gaussian Random Field (GRF) models based on the Sparse Precision matrix Selection (SPS) algorithm, proposed by Davanloo et al. (2015) for estimating scalar GRF models. Theoretical convergence rates for the estimated between-response covariance matrix and for the estimated parameters of the underlying spatial correlation function are established. Numerical tests using simulated and real datasets validate our theoretical findings. Data segmentation is used to handle large data sets.
Cite
@article{arxiv.1605.03267,
title = {Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets},
author = {Sam Davanloo Tajbakhsh and Necdet Serhat Aybat and Enrique del Castillo},
journal= {arXiv preprint arXiv:1605.03267},
year = {2021}
}