Reproducing kernel Hilbert spaces and local polynomial estimation of smooth functionals

We outline a general method that estimates smooth functionals of a probability distribution from a sample of observations, restricting the framework to local polynomial fitting. The construction of the estimators is based on a weighted least squares criterion and reproducing kernel Hilbert spaces theory. We briefly discuss their asymptotic properties and review applications to classical bivariate risk measures estimation.