Local moment matching with Erlang mixtures under automatic roughness penalization

Abstract

We consider the class of Erlang mixtures for the task of density estimation on the positive real line when the only available information is given as local moments, a histogram with potentially higher order moments in some bins. By construction, the obtained moment problem is ill-posed and requires regularization. Several penalties can be used for such a task, such as a lasso penalty for sparsity of the representation, but we focus here on a simplified roughness penalty from the P-splines literature. We show that the corresponding hyperparameter can be selected without cross-validation through the computation of the so-called effective dimension of the estimator, which makes the estimator practical and adapted to these summarized information settings. The flexibility of the local moments representations allows interesting additions such as the enforcement of Value-at-Risk and Tail Value-at-Risk constraints on the resulting estimator, making the procedure suitable for the estimation of heavy-tailed densities.