Nonparametric prediction in the multivariate spatial context

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

Type de document :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1080/10485252.2016.01.007

Titre :

Nonparametric prediction in the multivariate spatial context

Auteur(s) :

Dabo-Niang, Sophie [Auteur]
Lille économie management - UMR 9221 [LEM]
MOdel for Data Analysis and Learning [MODAL]
Ternynck, Camille [Auteur]

Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Yao, Anne-Françoise [Auteur]
Département de Mathématiques Informatique [Aubière]

Titre de la revue :

Journal of Nonparametric Statistics

Pagination :

428-458

Éditeur :

American Statistical Association

Date de publication :

2016-04

ISSN :

1048-5252

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [en]

This paper investigates a nonparametric spatial predictor of a stationary multidimensional spatial process observed over a rectangular domain. The proposed predictor depends on two kernels in order to control both the ...
Lire la suite >This paper investigates a nonparametric spatial predictor of a stationary multidimensional spatial process observed over a rectangular domain. The proposed predictor depends on two kernels in order to control both the distance between observations and that between spatial locations. The uniform almost complete consistency and the asymptotic normality of the kernel predictor are obtained when the sample considered is an alpha-mixing sequence. Numerical studies were carried out in order to illustrate the behaviour of our methodology both for simulated data and for an environmental data set.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL

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