Target estimation is a statistical procedure aimed at improving the bias and the variability of a statistic. This method has been proved to be effective in reducing the bias as well as the $L_1$  and $L_2$  errors of statistics in parametric settings.

We review the properties of target estimators both in the univariate and multivariate settings. We use the von Mises expansions of the corresponding functionals for analyzing the properties of the target estimators and for obtaining their asymptotic normality. The influence functions are obtained and used for robustness considerations.  Comparison studies with the jackknife and the bootstrap estimators as well as examples with applications to univariate and multivariate cases will be presented.