Robust density estimation with the L₁-loss. Applications to the estimation of a density on the line satisfying a shape constraint

We tackle the problem of estimating the distribution of presumed i.i.d. observations for the total variation loss. Our approach is based on density models and is versatile enough to cope with many different ones, including some for which the Maximum Likelihood Estimator (MLE for short) does not exist. We mainly illustrate the properties of our estimator on models of densities on the line that satisfy a shape constraint. We show that it possesses some similar optimality properties, with regard to some global rates of convergence, as the MLE does when it exists. It also enjoys some adaptation properties with respect to some specific densities in the model for which our estimator is proven to converge at the parametric rate 1/√n. More important is the fact that our estimator is robust, not only with respect to model misspecification, but also to contamination, the presence of outliers among the dataset and the equidistribution assumption we started from. This means that the estimator performs almost as well as if the data were i.i.d. with density p in a situation where these data are only independent and most of their marginals are close enough in total variation to a distribution with density p. We also show that our estimator converges to the average density of the data, when this density belongs to the model, even when none of the marginal densities does. Our main result on the risk of the estimator takes the form of an exponential deviation inequality which is nonasymptotic and involves explicit numerical constants. We deduce from it several global rates of convergence, including some bounds for the minimax L₁-risks over the sets of concave, log-concave or more generally s-concave densities with s > −1. These bounds derive from some approximation results of densities which are monotone, convex, concave or more generally s-concave. These results may be of independent interest.