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arXiv · 1605.03267

Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets

Sam Davanloo Tajbakhsh·Necdet Serhat Aybat·Enrique del Castillo
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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.

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Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique del Castillo. 2016-05-11. Generalized Sparse Precision Matrix Selection for Fitting Multivariate Gaussian Random Fields to Large Data Sets. https://doi.org/10.5705/ss.202017.0091

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