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• W3205284039 abstract "In this work, we propose a new approach to model large, irregularly distributed spatio-temporal global data via a locally diffusive Stochastic Partial Differential Equation (SPDE). The proposed model assumes a local deformation of the SPDE with nonlinear dependence on the covariates through a neural network. The proposed model can be fit in a computationally efficient manner using a triangulation over the sphere and sparsity of the precision matrix, as shown in an application with a large data set of simulated multi-decadal monthly sea surface temperature." @default.
• W3205284039 created "2021-10-25" @default.
• W3205284039 creator A5048011656 @default.
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• W3205284039 date "2022-03-09" @default.
• W3205284039 modified "2023-09-29" @default.
• W3205284039 title "A stochastic locally diffusive model with neural network‐based deformations for global sea surface temperature" @default.
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• W3205284039 doi "https://doi.org/10.1002/sta4.431" @default.
• W3205284039 hasPublicationYear "2022" @default.
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