A new spatial regression estimator in the ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
A new spatial regression estimator in the multivariate context
Auteur(s) :
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Yao, Anne-Françoise [Auteur]
Ternynck, Camille [Auteur]
Economie Quantitative, Intégration, Politiques Publiques et Econométrie [EQUIPPE]
MOdel for Data Analysis and Learning [MODAL]
Yao, Anne-Françoise [Auteur]
Ternynck, Camille [Auteur]
Economie Quantitative, Intégration, Politiques Publiques et Econométrie [EQUIPPE]
Titre de la revue :
Comptes rendus de l'Académie des sciences. Série I, Mathématique
Pagination :
635 - 639
Éditeur :
Elsevier
Date de publication :
2015-04-10
ISSN :
0764-4442
Discipline(s) HAL :
Mathématiques [math]/Statistiques [math.ST]
Résumé en anglais : [en]
In this note, we propose a nonparametric spatial estimator of the regression function View the MathML sourcex→r(x):=E[Yi|Xi=x],x∈Rd, of a stationary (d+1)(d+1)-dimensional spatial process View the MathML source{(Yi,Xi),i∈ZN}, ...
Lire la suite >In this note, we propose a nonparametric spatial estimator of the regression function View the MathML sourcex→r(x):=E[Yi|Xi=x],x∈Rd, of a stationary (d+1)(d+1)-dimensional spatial process View the MathML source{(Yi,Xi),i∈ZN}, 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 LqLq norm (q∈N⁎)(q∈N⁎) of the kernel estimate are obtained when the sample considered is an α-mixing sequence.Lire moins >
Lire la suite >In this note, we propose a nonparametric spatial estimator of the regression function View the MathML sourcex→r(x):=E[Yi|Xi=x],x∈Rd, of a stationary (d+1)(d+1)-dimensional spatial process View the MathML source{(Yi,Xi),i∈ZN}, 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 LqLq norm (q∈N⁎)(q∈N⁎) of the kernel estimate are obtained when the sample considered is an α-mixing sequence.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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