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Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping

Bolin, David LU and Lindgren, Finn LU (2011) In Annals of Applied Statistics 5(1). p.523-550
Abstract
A new class of stochastic field models is constructed using nested stochastic partial differential equations (SPDEs). The model class is computationally efficient, applicable to data on general smooth manifolds, and includes both the Gaussian Matérn fields and a wide family of fields with oscillating covariance functions. Nonstationary covariance models are obtained by spatially varying the parameters in the SPDEs, and the model parameters are estimated using direct numerical optimization, which is more efficient than standard Markov Chain Monte Carlo procedures. The model class is used to estimate daily ozone maps using a large data set of spatially irregular global total column ozone data.
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
total column ozone data, nonstationary covariances, Matérn covariances, Nested SPDEs
in
Annals of Applied Statistics
volume
5
issue
1
pages
523 - 550
publisher
Institute of Mathematical Statistics
external identifiers
  • wos:000295451800033
  • scopus:79961043012
ISSN
1932-6157
DOI
10.1214/10-AOAS383
project
MERGE
language
English
LU publication?
yes
id
a7dd3b47-9bdf-47b8-8467-559a22c3b005 (old id 1859142)
date added to LUP
2011-08-29 08:21:47
date last changed
2017-11-05 03:14:11
@article{a7dd3b47-9bdf-47b8-8467-559a22c3b005,
  abstract     = {A new class of stochastic field models is constructed using nested stochastic partial differential equations (SPDEs). The model class is computationally efficient, applicable to data on general smooth manifolds, and includes both the Gaussian Matérn fields and a wide family of fields with oscillating covariance functions. Nonstationary covariance models are obtained by spatially varying the parameters in the SPDEs, and the model parameters are estimated using direct numerical optimization, which is more efficient than standard Markov Chain Monte Carlo procedures. The model class is used to estimate daily ozone maps using a large data set of spatially irregular global total column ozone data.},
  author       = {Bolin, David and Lindgren, Finn},
  issn         = {1932-6157},
  keyword      = {total column ozone data,nonstationary covariances,Matérn covariances,Nested SPDEs},
  language     = {eng},
  number       = {1},
  pages        = {523--550},
  publisher    = {Institute of Mathematical Statistics},
  series       = {Annals of Applied Statistics},
  title        = {Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping},
  url          = {http://dx.doi.org/10.1214/10-AOAS383},
  volume       = {5},
  year         = {2011},
}