Nonparametric Statistical Analysis of Spatially Distributed Functional Data

Sophie Dabo-Niang; Camille Ternynck; Baba Thiam; Anne Françoise Yao

doi:10.1002/9781119387916.ch8

Nonparametric Statistical Analysis of Spatially Distributed Functional Data

Sophie Dabo-Niang
, Camille Ternynck
, Baba Thiam
, Anne Françoise Yao

Université de Lille
Clermont-Auvergne University

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Chapitre › Revue par des pairs

Résumé

A nonparametric estimator of the regression function of a scalar spatial variable, given a functional spatial variable is proposed. Mean square and almost complete consistencies of the estimator are obtained when the sample considered is an α-mixing sequence and composed of non-i.i.d observations. Lastly, an application to spatial prediction and numerical results is provided to illustrate the behavior of our estimator.

langue originale	Anglais
titre	Geostatistical Functional Data Analysis
Editeur	wiley
Pages	175-210
Nombre de pages	36
ISBN (Electronique)	9781119387916
ISBN (imprimé)	9781119387848
Les DOIs	https://doi.org/10.1002/9781119387916.ch8
état	Publié - 1 janv. 2021
Modification externe	Oui

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10.1002/9781119387916.ch8

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title = "Nonparametric Statistical Analysis of Spatially Distributed Functional Data",

abstract = "A nonparametric estimator of the regression function of a scalar spatial variable, given a functional spatial variable is proposed. Mean square and almost complete consistencies of the estimator are obtained when the sample considered is an α-mixing sequence and composed of non-i.i.d observations. Lastly, an application to spatial prediction and numerical results is provided to illustrate the behavior of our estimator.",

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AU - Thiam, Baba

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AB - A nonparametric estimator of the regression function of a scalar spatial variable, given a functional spatial variable is proposed. Mean square and almost complete consistencies of the estimator are obtained when the sample considered is an α-mixing sequence and composed of non-i.i.d observations. Lastly, an application to spatial prediction and numerical results is provided to illustrate the behavior of our estimator.

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Nonparametric Statistical Analysis of Spatially Distributed Functional Data

Résumé

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