The aim of this paper is to present a new estimation procedure that can be applied in many statistical frameworks including density and regression and which leads to both robust and optimal (or nearly optimal) estimators. In density estimation, they asymptotically coincide with the celebrated maximum likelihood estimators at least when the statistical model is regular enough and contains the true density to estimate. For very general models of densities, including non-compact ones, these estimators are robust with respect to the Hellinger distance and converge at optimal rate (up to a possible logarithmic factor) in all cases we know. In the regression setting, our approach improves upon the classical least squares from many aspects. In simple linear regression for example, it provides an estimation of the coefficients that are both robust to outliers and simultaneously rate-optimal (or nearly rate-optimal) for large class of error distributions including Gaussian, Laplace, Cauchy and uniform among others.