Comptes Rendus
Statistics
A new spatial regression estimator in the multivariate context
Comptes Rendus. Mathématique, Volume 353 (2015) no. 7, pp. 635-639.

In this note, we propose a nonparametric spatial estimator of the regression function xr(x):=E[Yi|Xi=x],xRd, of a stationary (d+1)-dimensional spatial process {(Yi,Xi),iZN}, at a point located at some station j. The proposed estimator depends on two kernels in order to control both the distance between observations and the spatial locations. Almost complete convergence and consistency in Lq norm (qN) of the kernel estimate are obtained when the sample considered is an α-mixing sequence.

Dans cette note, nous proposons un estimateur non paramétrique spatial de la fonction de régression xr(x):=E[Yi|Xi=x],xRd, d'un champ stationnaire {(Yi,Xi),iZN} de dimension (d+1), à un point localisé à un site donné j. L'estimateur proposé est composé de deux noyaux permettant de contrôler à la fois la distance entre les observations et entre les sites. La convergence presque complète ainsi que la convergence en moyenne d'ordre q (norme Lq) (qN) de l'estimateur à noyaux sont obtenus en considérant des processus α-mélangeants.

Received:
Accepted:
Published online:
DOI: 10.1016/j.crma.2015.04.004
Sophie Dabo-Niang 1, 2; Camille Ternynck 1; Anne-Francoise Yao 3

1 Laboratoire LEM, Université Lille-3, Maison de la recherche, BP 60149, 59653 Villeneuve d'Ascq cedex, France
2 MODAL team, INRIA Lille-Nord de France, France
3 Laboratoire de Mathématiques, Université Blaise-Pascal, UMR 6620, CNRS, Campus des Cézeaux, BP 80026, 63171 Aubière cedex, France
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Sophie Dabo-Niang; Camille Ternynck; Anne-Francoise Yao. A new spatial regression estimator in the multivariate context. Comptes Rendus. Mathématique, Volume 353 (2015) no. 7, pp. 635-639. doi : 10.1016/j.crma.2015.04.004. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2015.04.004/

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