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Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping. (English) Zbl 1235.60075
The authors construct a new flexible class of spatial models using nested stochastic partial differential equations (SPDE). This model class contains a wide family of covariance functions, including both the Matérn family and oscillating covariance functions, and it maintains all desirable properties of the Markov approximated Matérn model, such as computational efficiency, easy nonstationary extendibility and applicability to data on general smooth manifolds. The model class is used to estimate daily ozone maps using a large data set of spatially irregular global total column ozone data.

MSC:
60H15 Stochastic partial differential equations (aspects of stochastic analysis)
35R60 PDEs with randomness, stochastic partial differential equations
Software:
GMRFLib; FRK
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