Optimal Spatial Prediction Using Ensemble Machine Learning

Davies Molly Margaret () and J. van der Laan Mark
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Davies Molly Margaret: Group in Biostatistics, University of California, Berkeley, Berkeley, CA, USA
J. van der Laan Mark: Group in Biostatistics, University of California, Berkeley, Berkeley, CA, USA

The International Journal of Biostatistics, 2016, vol. 12, issue 1, 179-201

Abstract: Spatial prediction is an important problem in many scientific disciplines. Super Learner is an ensemble prediction approach related to stacked generalization that uses cross-validation to search for the optimal predictor amongst all convex combinations of a heterogeneous candidate set. It has been applied to non-spatial data, where theoretical results demonstrate it will perform asymptotically at least as well as the best candidate under consideration. We review these optimality properties and discuss the assumptions required in order for them to hold for spatial prediction problems. We present results of a simulation study confirming Super Learner works well in practice under a variety of sample sizes, sampling designs, and data-generating functions. We also apply Super Learner to a real world dataset.

Keywords: cross-validation; spatial interpolation; generalized stacking; oracle inequality; Super Learner (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1515/ijb-2014-0060

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