Nonparametric prediction in the multivariate spatial context

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

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1080/10485252.2016.01.007

Title :

Nonparametric prediction in the multivariate spatial context

Author(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]

Journal title :

Journal of Nonparametric Statistics

Pages :

428-458

Publisher :

American Statistical Association

Publication date :

2016-04

ISSN :

1048-5252

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]

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

Language :

Anglais

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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