Large datasets with irregularly spatial (or spatio-temporal) locations are difficult to handle in many applications of Gaussian random fields, such as maxi- mum likelihood estimation (MLE) and prediction. We aim to approximate covariance functions in a format that facilitates the computation of MLE and prediction with very large datasets using a hierarchical matrix approach. We present a numerical study where we compare this approach with the covariance tapering method.

Application of hierarchical matrices in spatial statistics

Gorshechnikova, Anastasiia
;
Gaetan ,Carlo
2021

Abstract

Large datasets with irregularly spatial (or spatio-temporal) locations are difficult to handle in many applications of Gaussian random fields, such as maxi- mum likelihood estimation (MLE) and prediction. We aim to approximate covariance functions in a format that facilitates the computation of MLE and prediction with very large datasets using a hierarchical matrix approach. We present a numerical study where we compare this approach with the covariance tapering method.
Book of short papers - SIS 2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3745904
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