Model-calibration estimation of the distribution function using nonparametric regression

Nonparametric regression has only recently been employed in the estimation of finite population parameters in a model-assisted framework. This paper proposes a new calibration estimator for the distribution function using nonparametric methods to obtain the fitted values on which to calibrate. The proposed estimator is a genuine distribution function that presents several attractive features. In terms of relative efficiency and relative bias, the behaviour of the proposed estimator is compared to other known estimators in a limited simulation study on real populations.