Smooth Conditional Distribution Function and Quantiles under Random Censorship |
| |
Authors: | Leconte Eve Poiraud-Casanova Sandrine Thomas-Agnan Christine |
| |
Affiliation: | (1) G.R.E.M.A.Q., Université des Sciences Sociales, 21 allée de Brienne, 31000 Toulouse, France;(2) L.S.P., Université Paul Sabatier, 118 route de Narbonne, 31000 Toulouse, France |
| |
Abstract: | We consider a nonparametric random design regression model in which the response variable is possibly right censored. The aim of this paper is to estimate the conditional distribution function and the conditional -quantile of the response variable. We restrict attention to the case where the response variable as well as the explanatory variable are unidimensional and continuous. We propose and discuss two classes of estimators which are smooth with respect to the response variable as well as to the covariate. Some simulations demonstrate that the new methods have better mean square error performances than the generalized Kaplan-Meier estimator introduced by Beran (1981) and considered in the literature by Dabrowska (1989, 1992) and Gonzalez-Manteiga and Cadarso-Suarez (1994). |
| |
Keywords: | censored data conditional quantile generalized Kaplan-Meier estimator nonparametric estimation smoothing techniques |
本文献已被 PubMed SpringerLink 等数据库收录! |