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

David Bolin (Institutionen för matematiska vetenskaper, matematisk statistik) ; Finn Lindgren
Annals of Applied Statistics (1932-6157). Vol. 5 (2011), 1, p. 523-550.
[Artikel, refereegranskad vetenskaplig]

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. © Institute of Mathematical Statistics, 2011.

Nyckelord: Matérn covariances , Nested SPDEs , Nonstationary covariances , Total column ozone data



Denna post skapades 2014-01-08. Senast ändrad 2015-02-26.
CPL Pubid: 191829

 

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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)

Ämnesområden

Matematisk statistik

Chalmers infrastruktur