Kernel Inverse Regression for spatial random fields

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dc.creatorLoubes, Jean-Michel
dc.creatorYao, Anne-Françoise
dc.date2008-12-17
dc.date.accessioned2026-07-07T12:16:14Z
dc.date.available2026-07-07T12:16:14Z
dc.descriptionIn 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.
dc.identifierhttps://arxiv.org/abs/0812.3254
dc.identifierhttp://arxiv.org/abs/0812.3254
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/211736
dc.subjectStatistics Theory
dc.titleKernel Inverse Regression for spatial random fields
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