Pseudo-empirical likelihood estimation using nonparametric regression

Pseudo-empirical likelihood estimation of the population mean is considered. A nonparametric regression theory is proposed, to provide the fitted values on which to calibrate, and the common model misspecification problem is therefore addressed. Results derived from empirical studies show that the proposed estimator for the population mean can perform better than alternative estimators.