NONSEPARABLE DYNAMIC NEAREST NEIGHBOR GAUSSIAN PROCESS MODELS FOR LARGE SPATIO-TEMPORAL DATA WITH AN APPLICATION TO PARTICULATE MATTER ANALYSIS.

@article{Datta2015NONSEPARABLEDN,
  title={NONSEPARABLE DYNAMIC NEAREST NEIGHBOR GAUSSIAN PROCESS MODELS FOR LARGE SPATIO-TEMPORAL DATA WITH AN APPLICATION TO PARTICULATE MATTER ANALYSIS.},
  author={Abhirup Datta and Sudipto Banerjee and Andrew O. Finley and Nicholas A. S. Hamm and Martijn Schaap},
  journal={The annals of applied statistics},
  year={2015},
  volume={10 3},
  pages={
          1286-1316
        }
}
Particulate matter (PM) is a class of malicious environmental pollutants known to be detrimental to human health. Regulatory efforts aimed at curbing PM levels in different countries often require high resolution space-time maps that can identify red-flag regions exceeding statutory concentration limits. Continuous spatio-temporal Gaussian Process (GP) models can deliver maps depicting predicted PM levels and quantify predictive uncertainty. However, GP-based approaches are usually thwarted by… 
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