A Case Study Competition Among Methods for Analyzing Large Spatial Data

@article{Heaton2019ACS,
  title={A Case Study Competition Among Methods for Analyzing Large Spatial Data},
  author={Matthew J. Heaton and Abhirup Datta and Andrew O. Finley and Reinhard Furrer and Joseph Guinness and Rajarshi Guhaniyogi and Florian Gerber and Robert B. Gramacy and Dorit M. Hammerling and Matthias Katzfuss and Finn Lindgren and Douglas W. Nychka and Furong Sun and Andrew Zammit‐Mangion},
  journal={Journal of Agricultural, Biological, and Environmental Statistics},
  year={2019},
  volume={24},
  pages={398 - 425}
}
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data” era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This… 

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