Strong consistency result of a non parametric ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Strong consistency result of a non parametric conditional mode estimator under random censorship for functional regressors
Auteur(s) :
Titre de la revue :
Communications in Statistics - Theory and Methods
Pagination :
1863--1875
Éditeur :
Taylor & Francis
Date de publication :
2016-03
ISSN :
0361-0926
Mot(s)-clé(s) en anglais :
Almost complete convergence
censored data
functional data
Kaplan–Meier estimator
kernel mode estimator
strong mixing condition
censored data
functional data
Kaplan–Meier estimator
kernel mode estimator
strong mixing condition
Discipline(s) HAL :
Sciences de l'Homme et Société/Méthodes et statistiques
Résumé en anglais : [en]
Let (T, C, X) be a vector of random variables (rvs) where T, C, and X are the interest variable, a right censoring rv, and a covariate, respectively. In this paper, we study the kernel conditional mode estimation when the ...
Lire la suite >Let (T, C, X) be a vector of random variables (rvs) where T, C, and X are the interest variable, a right censoring rv, and a covariate, respectively. In this paper, we study the kernel conditional mode estimation when the covariate takes values in an infinite dimensional space and is α-mixing. Under some regularity conditions, the almost complete convergence of the estimate with rates is established.Lire moins >
Lire la suite >Let (T, C, X) be a vector of random variables (rvs) where T, C, and X are the interest variable, a right censoring rv, and a covariate, respectively. In this paper, we study the kernel conditional mode estimation when the covariate takes values in an infinite dimensional space and is α-mixing. Under some regularity conditions, the almost complete convergence of the estimate with rates is established.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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