Quasi-maximum Likelihood Estimators for ...
Type de document :
Partie d'ouvrage
Titre :
Quasi-maximum Likelihood Estimators for Functional Linear Spatial Autoregressive Models
Auteur(s) :
AHMED, Mohamed-Salem [Auteur]
Broze, Laurence [Auteur]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Gharbi, Zied [Auteur]
Lille économie management - UMR 9221 [LEM]
Broze, Laurence [Auteur]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Gharbi, Zied [Auteur]
Lille économie management - UMR 9221 [LEM]
Éditeur(s) ou directeur(s) scientifique(s) :
Ramon Giraldo
Jorge Mateu
Jorge Mateu
Titre de l’ouvrage :
Geostatistical Functional Data Analysis: Theory and Methods
Éditeur :
Wiley
Date de publication :
2021
ISBN :
978-1-119-38784-8
Discipline(s) HAL :
Mathématiques [math]
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Statistiques [math.ST]
Résumé en anglais : [en]
A functional linear autoregressive spatial model, where the explanatory variable takes values in a function space, while the response process is real-valued and spatially autocorrelated, is proposed. The specificity of the ...
Lire la suite >A functional linear autoregressive spatial model, where the explanatory variable takes values in a function space, while the response process is real-valued and spatially autocorrelated, is proposed. The specificity of the model is due to the functional nature of the explanatory variable and the structure of a spatial weight matrix that defines the spatial dependency between neighbors. The estimation procedure consists of reducing the infinite dimension of the functional explanatory variable and maximizing the quasi-maximum likelihood. We establish the consistency and asymptotic normality of the estimator. The ability of the methodology is illustrated via simulations and by application to real data.Lire moins >
Lire la suite >A functional linear autoregressive spatial model, where the explanatory variable takes values in a function space, while the response process is real-valued and spatially autocorrelated, is proposed. The specificity of the model is due to the functional nature of the explanatory variable and the structure of a spatial weight matrix that defines the spatial dependency between neighbors. The estimation procedure consists of reducing the infinite dimension of the functional explanatory variable and maximizing the quasi-maximum likelihood. We establish the consistency and asymptotic normality of the estimator. The ability of the methodology is illustrated via simulations and by application to real data.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Collections :
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