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- Kernel Inverse Regression for spatial random fields arxiv link

Auteur(s): Loubes Jean-Michel, Yao A.-F.(Corresp.)

(Document sans référence bibliographique) 2008-12-16


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Résumé:

In this paper, we propose a dimension reduction model for spatially dependent variables. Namely, we investigate an extension of the emph{inverse regression} method under strong mixing condition. This method is based on estimation of the matrix of covariance of the expectation of the explanatory given the dependent variable, called the emph{inverse regression}. Then, we study, under strong mixing condition, the weak and strong consistency of this estimate, using a kernel estimate of the emph{inverse regression}. We provide the asymptotic behaviour of this estimate. A spatial predictor based on this dimension reduction approach is also proposed. This latter appears as an alternative to the spatial non-parametric predictor.