Semiparametric regression models for spatial prediction and uncertainty quantification of soil attributes

@article{Merrill2016SemiparametricRM,
  title={Semiparametric regression models for spatial prediction and uncertainty quantification of soil attributes},
  author={Hunter R. Merrill and Sabine Grunwald and Nikolay Bliznyuk},
  journal={Stochastic Environmental Research and Risk Assessment},
  year={2016},
  volume={31},
  pages={2691-2703}
}
In many studies, the distribution of soil attributes depends on both spatial location and environmental factors, and prediction and process identification are performed using existing methods such as kriging. However, it is often too restrictive to model soil attributes as dependent on a known, parametric function of environmental factors, which kriging typically assumes. This paper investigates a semiparametric approach for identifying and modeling the nonlinear relationships of spatially… 
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