A linear mixed model formulation for spatio-temporal random processes with computational advances for the product, sum, and product–sum covariance functions

@article{Dumelle2020ALM,
  title={A linear mixed model formulation for spatio-temporal random processes with computational advances for the product, sum, and product–sum covariance functions},
  author={Michael Dumelle and Jay M. Ver Hoef and Claudio Fuentes and Alix I. Gitelman},
  journal={arXiv: Methodology},
  year={2020}
}
We describe spatio-temporal random processes using linear mixed models. We show how many commonly used models can be viewed as special cases of this general framework and pay close attention to models with separable or product-sum covariances. The proposed linear mixed model formulation facilitates the implementation of a novel algorithm using Stegle eigendecompositions, a recursive application of the Sherman-Morrison-Woodbury formula, and Helmert-Wolf blocking to efficiently invert separable… 

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