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Author (up) Morales-Onate, V.; Crudu, F.; Bevilacqua, M. doi  openurl
  Title Blockwise Euclidean likelihood for spatio-temporal covariance models Type
  Year 2021 Publication Econometrics and Statistics Abbreviated Journal Econ. Stat.  
  Volume 20 Issue Pages 176-201  
  Keywords Composite likelihood; Euclidean likelihood; Gaussian random fields; Parallel computing; OpenCL  
  Abstract 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. (C) 2021 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.  
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  ISSN 2468-0389 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000689351000012 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1460  
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