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Fast kriging of large data sets with Gaussian Markov random fields

Werner Hartman, Linda LU and Hössjer, Ola (2008) In Computational Statistics & Data Analysis 52(5). p.2331-2349
Abstract (Swedish)
Abstract in Undetermined

patial data sets are analysed in many scientific disciplines. Kriging, i.e. minimum mean squared error linear prediction, is probably the most widely used method of spatial prediction. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field on a lattice as an approximation of a Gaussian field. The algorithms are well suited also for nonlattice data when exploiting a bilinear interpolation at nonlattice locations. (c) 2007 Elsevier B.V. All rights reserved.
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
nonlattice data, Markov random field, spatial interpolation, bilinear interpolation
in
Computational Statistics & Data Analysis
volume
52
issue
5
pages
2331 - 2349
publisher
Elsevier
external identifiers
  • wos:000253178600005
  • scopus:38149002917
ISSN
0167-9473
DOI
10.1016/j.csda.2007.09.018
language
English
LU publication?
yes
id
d35d91d5-b16e-47cf-83d7-8d2caa32f07f (old id 754762)
date added to LUP
2008-01-04 14:18:33
date last changed
2017-07-09 03:48:29
@article{d35d91d5-b16e-47cf-83d7-8d2caa32f07f,
  abstract     = {<b>Abstract in Undetermined</b><br/><br>
patial data sets are analysed in many scientific disciplines. Kriging, i.e. minimum mean squared error linear prediction, is probably the most widely used method of spatial prediction. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field on a lattice as an approximation of a Gaussian field. The algorithms are well suited also for nonlattice data when exploiting a bilinear interpolation at nonlattice locations. (c) 2007 Elsevier B.V. All rights reserved.},
  author       = {Werner Hartman, Linda and Hössjer, Ola},
  issn         = {0167-9473},
  keyword      = {nonlattice data,Markov random field,spatial interpolation,bilinear interpolation},
  language     = {eng},
  number       = {5},
  pages        = {2331--2349},
  publisher    = {Elsevier},
  series       = {Computational Statistics & Data Analysis},
  title        = {Fast kriging of large data sets with Gaussian Markov random fields},
  url          = {http://dx.doi.org/10.1016/j.csda.2007.09.018},
  volume       = {52},
  year         = {2008},
}