arXiv Methodology
arXiv Methodology
Preprint
4 years ago

Random Spatial Forests. (arXiv:2006.00150v2 [stat.ME] UPDATED)

Travis Hee Wai, Michael T. Young, Adam A. Szpiro
We introduce random spatial forests, a method of bagging regression trees allowing for spatial correlation. Our main contribution is the development of a computationally efficient tree building algorithm which selects each split of the tree adjusting for spatial correlation. We evaluate two different approaches for estimation of random spatial forests, a pseudo-likelihood approach combining random forests with kriging and a non-parametric version for a general class of spatial smoothers. We show improved prediction accuracy of our method compared to existing two-step approaches combining random forests and kriging across a range of numerical simulations and demonstrate its performance on elemental carbon, organic carbon, silicon, and sulfur measurements across the continental United States from 2009-2010.

Publisher URL: http://arxiv.org/abs/2006.00150

DOI: arXiv:2006.00150v2

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