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A comparison betwee...
A comparison between Markov approximations and other methods for large spatial data sets
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- Bolin, David (författare)
- Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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- Lindgren, Finn (författare)
- Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- Elsevier BV, 2013
- 2013
- Engelska.
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473. ; 61, s. 7-21
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Abstract
Ämnesord
Stäng
- The Matern covariance function is a popular choice for modeling dependence in spatial environmental data. Standard Matern covariance models are, however, often computationally infeasible for large data sets. Recent results for Markov approximations of Gaussian Matern fields based on Hilbert space approximations are extended using wavelet basis functions. Using a simulation-based study, these Markov approximations are compared with two of the most popular methods for computationally efficient model approximations, covariance tapering and the process convolution method. The methods are compared with respect to their computational properties when used for spatial prediction (kriging), and the results show that, for a given computational cost, the Markov methods have a substantial gain in accuracy compared with the other methods. (C) 2012 Elsevier B.V. All rights reserved.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Nyckelord
- Matern covariances
- Kriging
- Wavelets
- Markov random fields
- Covariance
- tapering
- Process convolutions
- Wavelets
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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