Title: Nonparametric relative error estimation of the regression function for censored data,
Abstract: Let $ (T_i)_i$ be a sequence of independent identically distributed (i.i.d.)
random variables (r.v.) of interest distributed as $ T$ and $(X_i)_i$ be a
corresponding vector of covariates taking values on $ \mathbb{R}^d$. In
censorship models the r.v. $T$ is subject to random censoring by another r.v.
$C$. In this paper we built a new kernel estimator based on the so-called
synthetic data of the mean squared relative error for the regression function.
We establish the uniform almost sure convergence with rate over a compact set
and its asymptotic normality. The asymptotic variance is explicitly given and
as product we give a confidence bands. A simulation study has been conducted to
comfort our theoretical results