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• W2205619076 startingPage "117" @default.
• W2205619076 abstract "The statistical analysis of data belonging to Riemannian manifolds is becoming increasingly important in many applications, such as shape analysis, diffusion tensor imaging and the analysis of covariance matrices. In many cases, data are spatially distributed but it is not trivial to take into account spatial dependence in the analysis because of the non linear geometry of the manifold. This work proposes a solution to the problem of spatial prediction for manifold valued data, with a particular focus on the case of positive definite symmetric matrices. Under the hypothesis that the dispersion of the observations on the manifold is not too large, data can be projected on a suitably chosen tangent space, where an additive model can be used to describe the relationship between response variable and covariates. Thus, we generalize classical kriging prediction, dealing with the spatial dependence in this tangent space, where well established Euclidean methods can be used. The proposed kriging prediction is applied to the matrix field of covariances between temperature and precipitation in Quebec, Canada." @default.
• W2205619076 created "2016-06-24" @default.
• W2205619076 creator A5016716189 @default.
• W2205619076 creator A5063994264 @default.
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• W2205619076 date "2016-03-01" @default.
• W2205619076 modified "2023-09-27" @default.
• W2205619076 title "Kriging prediction for manifold-valued random fields" @default.
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• W2205619076 doi "https://doi.org/10.1016/j.jmva.2015.12.006" @default.
• W2205619076 hasPublicationYear "2016" @default.
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