On Prediction Properties of Kriging: Uniform Error Bounds and Robustness Article Swipe

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YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.6084/m9.figshare.7895756.v2

Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are computationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work, we obtain some error bounds for the simple and universal kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case where the covariance function of the Gaussian process is misspecified. These results lead to a better understanding of the rate of convergence of kriging under the Gaussian or the Matérn correlation functions, the relationship between space-filling designs and kriging models, and the robustness of the Matérn correlation functions. Supplementary materials for this article are available online.

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Robustness (evolution) Kriging Computer science Mathematics Mean squared prediction error Algorithm Applied mathematics Statistics Chemistry Biochemistry Gene

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Type: dataset
Language: en
Landing Page: https://doi.org/10.6084/m9.figshare.7895756.v2
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DOI: https://doi.org/10.6084/m9.figshare.7895756.v2

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Title: On Prediction Properties of Kriging: Uniform Error Bounds and Robustness

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Language: en

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Publication year: 2020

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Publication date: 2020-01-01

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Authors: Wenjia Wang, Rui Tuo, Changbao Wu

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Landing page: https://doi.org/10.6084/m9.figshare.7895756.v2

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OA status: gold

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OA URL: https://doi.org/10.6084/m9.figshare.7895756.v2

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Concepts: Robustness (evolution), Kriging, Computer science, Mathematics, Mean squared prediction error, Algorithm, Applied mathematics, Statistics, Chemistry, Biochemistry, Gene

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