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

Bolin, David,1983Lund University,Lunds universitet,Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper, matematisk statistik,Department of Mathematical Sciences, Mathematical Statistics,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,Chalmers tekniska högskola,Chalmers University of Technology,University of Gothenburg (författare)

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.

Lindgren, FinnLund 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(Swepub:lu)mats-fli (författare)
Göteborgs universitetInstitutionen för matematiska vetenskaper, matematisk statistik (creator_code:org_t)

Om ämnet

Av lärosätet: Göteborgs universitet; Lunds universitet; Chalmers tekniska högskola

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