A new spatial regression estimator in the multivariate context

Dabo-Niang, Sophie; Yao, Anne-Françoise; Ternynck, Camille

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1016/j.crma.2015.04.004

Title :

A new spatial regression estimator in the multivariate context

Author(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]

Journal title :

Comptes rendus de l'Académie des sciences. Série I, Mathématique

Pages :

635 - 639

Publisher :

Elsevier

Publication date :

2015-04-10

ISSN :

0764-4442

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]

English abstract : [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}, ...
Show more >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.Show less >

Language :

Anglais

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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