[en] Suppose the random vector (X,Y) satisfies the nonparametric regression model Y=m(X)+sigma(X)*epsilon where m(X) =E [Y|X] and sigma^2(X) = Var [Y|X] are unknown smooth functions and the error epsilon, with unknown distribution, is independent of the covariate X. The pair (X,Y) is subject to generalized bias selection and the response Y to right censoring. We define a new estimator for the cumulative distribution function of the error epsilon, where the estimators of m(.) and sigma^2(.) are obtained by extending the conditional estimation methods introduced in de Uña-Alvarez and Iglesias-Perez (2010). The asymptotic properties of the proposed estimator are established. A bootstrap technique is proposed to select the smoothing parameter involved in the procedure. Finally, this method is studied via extended simulations and applied to real data.