Mean preservation in nonparametric regression with censored data

Consider the heteroscedastic model Y = m(X) + σ(X)ε, where ε and X are independent, Y is sub ject to right censoring, m(.) is an unknown but smooth location function (like e.g. conditional mean, median, trimmed mean...) and σ(.) an unknown but smooth scale function. In this paper we consider the estimation of m(.) under this model. The estimator we propose is a Nadaraya-Watson type estimator, for which the censored observations are replaced by `synthetic' data points estimated under the above model. The estimator offers an alternative for the completely nonparametric estimator of m(.), which cannot be estimated consistently in a completely nonparametric way , whenever high quantiles of the conditional distribution of Y given X = x are in volved. We obtain the asymptotic properties of the proposed estimator of m(x) and study its finite sample behavior in a simulation study . The method is also applied to a study of quasars in astronomy