Blockwise Euclidean likelihood for spatio-temporal covariance models

A spatio-temporal blockwise Euclidean likelihood method for the estimation of covariance models when dealing with large spatio-temporal Gaussian data is proposed. The method uses moment conditions coming from the score of the pairwise composite likelihood. The blockwise approach guarantees considera...

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Bibliographic Details
Published in:Econometrics and statistics 2021-10, Vol.20, p.176-201
Main Authors: Morales-Oñate, Víctor, Crudu, Federico, Bevilacqua, Moreno
Format: Article
Language:English
Subjects:
Composite likelihood
Euclidean likelihood
Gaussian random fields
OpenCL
Parallel computing
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Summary:A spatio-temporal blockwise Euclidean likelihood method for the estimation of covariance models when dealing with large spatio-temporal Gaussian data is proposed. The method uses moment conditions coming from the score of the pairwise composite likelihood. The blockwise approach guarantees considerable computational improvements over the standard pairwise composite likelihood method. In order to further speed up computation, a general purpose graphics processing unit implementation using OpenCL is implemented. The asymptotic properties of the proposed estimator are derived and the finite sample properties of this methodology by means of a simulation study highlighting the computational gains of the OpenCL graphics processing unit implementation. Finally, there is an application of the estimation method to a wind component data set.
ISSN:2452-3062
2452-3062
DOI:10.1016/j.ecosta.2021.01.001