How do Markov approximations compare with other methods for large spatial data sets?

David Bolin; Finn Lindgren

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arXiv · 1106.1980

How do Markov approximations compare with other methods for large spatial data sets?

David Bolin·Finn Lindgren

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Abstract

The Matérn covariance function is a popular choice for modeling dependence in spatial environmental data. Standard Matérn covariance models are, however, often computationally infeasible for large data sets. In this work, recent results for Markov approximations of Gaussian Matérn fields based on Hilbert space approximations are extended using wavelet basis functions. These Markov approximations are compared with two of the most popular methods for efficient covariance approximations; covariance tapering and the process convolution method. The results show that, for a given computational cost, the Markov methods have a substantial gain in accuracy compared with the other methods.

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David Bolin, Finn Lindgren. 2011-11-04. How do Markov approximations compare with other methods for large spatial data sets?. https://arxiv.org/abs/1106.1980

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