Titre :

Robust regression analysis for a censored response and functional regressors

Auteur(s) :

Hennani, L. Ait [Auteur]
Lemdani, Mohamed [Auteur]
Laboratoire de Biomathématiques
Said, Elias Ould [Auteur]

Titre de la revue :

Journal of nonparametric statistics

Nom court de la revue :

J. Nonparametr. Stat.

Numéro :

31

Pagination :

221-243

Éditeur :

Taylor & Francis

Date de publication :

2019-01-01

ISSN :

1048-5252

Mot(s)-clé(s) en anglais :

robust estimation
kernel estimator
Asymptotic normality
functional random variable
censored data

Discipline(s) HAL :

Sciences du Vivant [q-bio]

Résumé en anglais : [en]

Let (Tn)n≥1 be an independent and identically distributed (iid) sequence of interest random variables (rv) distributed as T. In censorship models, T is subject to random censoring by another rv C. Based on the so-called ...
Lire la suite >Let (Tn)n≥1 be an independent and identically distributed (iid) sequence of interest random variables (rv) distributed as T. In censorship models, T is subject to random censoring by another rv C. Based on the so-called synthetic data, we define an M-estimator for the regression function of T given a functional covariate χ. Under standard assumptions on the kernel, bandwidth and small ball probabilities, we establish its strong consistency with rate and asymptotic normality. The asymptotic variance is given explicitly. Confidence bands are given and special cases are studied to show the generality of our work. Finally simulations are drawn to illustrate both quality of fit and robustness.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

CHU Lille
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Date de dépôt :

2019-12-09T18:20:38Z
2024-04-10T09:09:18Z