Strong consistency result of a non parametric ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Strong consistency result of a non parametric conditional mode estimator under random censorship for functional regressors
Author(s) :
Journal title :
Communications in Statistics - Theory and Methods
Pages :
1863--1875
Publisher :
Taylor & Francis
Publication date :
2016-03
ISSN :
0361-0926
English keyword(s) :
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
HAL domain(s) :
Sciences de l'Homme et Société/Méthodes et statistiques
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
Collections :
Source :