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Nonparametric estimation of the multivariate distribution function in a censored regression model with applications

Olivier Lopez 1

(24/04/2010)

In a regression model with univariate censored responses, a new estimator of the joint distribution function of the covariates and response is proposed, under the assumption that the response and the censoring variable are independent conditionally to the covariates. This estimator is based on the conditional Kaplan-Meier estimator of Beran (1981), and happens to be an extension of the multivariate empirical distribution function used in the uncensored case. We derive asymptotic i.i.d. representations for the integrals with respect to the measure defined by this estimated distribution function. These representations hold even in the case where the covariates are multidimensional under some additional assumption on the censoring. Applications to censored regression and to density estimation are considered.

1 :	Laboratoire de Statistique Théorique et Appliquée (LSTA)
	Université Paris VI - Pierre et Marie Curie

Domaine

:

Mathématiques/Statistiques Statistiques/Théorie

Mots Clés : Multivariate distribution – Right censoring – Parametric regression – Survival analysis – Dimension reduction

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Contributeur : Olivier Lopez <>

Soumis le : Samedi 24 Avril 2010, 14:13:12

Dernière modification le : Dimanche 25 Avril 2010, 21:17:43