Efficient estimation of the error distribution function in heteroskedastic nonparametric regression with missing data

We propose a residual-based empirical distribution function to estimate the distribution function of the errors of a heteroskedastic nonparametric regression with responses missing at random based on completely observed data, and we show this estimator is asymptotically most precise