In this note, we propose a nonparametric spatial estimator of the regression function , of a stationary -dimensional spatial process , 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 norm 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 , d'un champ stationnaire de dimension , 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 ) de l'estimateur à noyaux sont obtenus en considérant des processus α-mélangeants.
Accepted:
Published online:
Sophie Dabo-Niang 1, 2; Camille Ternynck 1; Anne-Francoise Yao 3
@article{CRMATH_2015__353_7_635_0, author = {Sophie Dabo-Niang and Camille Ternynck and Anne-Francoise Yao}, title = {A new spatial regression estimator in the multivariate context}, journal = {Comptes Rendus. Math\'ematique}, pages = {635--639}, publisher = {Elsevier}, volume = {353}, number = {7}, year = {2015}, doi = {10.1016/j.crma.2015.04.004}, language = {en}, }
TY - JOUR AU - Sophie Dabo-Niang AU - Camille Ternynck AU - Anne-Francoise Yao TI - A new spatial regression estimator in the multivariate context JO - Comptes Rendus. Mathématique PY - 2015 SP - 635 EP - 639 VL - 353 IS - 7 PB - Elsevier DO - 10.1016/j.crma.2015.04.004 LA - en ID - CRMATH_2015__353_7_635_0 ER -
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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