Quasi-maximum Likelihood Estimators for ...
Document type :
Partie d'ouvrage
Title :
Quasi-maximum Likelihood Estimators for Functional Linear Spatial Autoregressive Models
Author(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]
Scientific editor(s) :
Ramon Giraldo
Jorge Mateu
Jorge Mateu
Book title :
Geostatistical Functional Data Analysis: Theory and Methods
Publisher :
Wiley
Publication date :
2021
ISBN :
978-1-119-38784-8
HAL domain(s) :
Mathématiques [math]
Mathématiques [math]/Statistiques [math.ST]
Mathématiques [math]/Statistiques [math.ST]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Collections :
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