Asymptotic minimum scoring rule prediction

Abstract

Most of the methods nowadays employed in forecast problems are based on scoring rules. There is a divergence function associated to each scoring rule, that can be used as a measure of discrepancy between probability distributions. This approach is commonly used in the literature for comparing two competing predictive distributions on the basis of their relative expected divergence from the true distribution.

In this paper we focus on the use of scoring rules as a tool for finding predictive distributions for an unknown of interest. The proposed predictive distributions are asymptotic modifications of the estimative solutions, obtained by minimizing the expected divergence related to a general scoring rule.

The asymptotic properties of such predictive distributions are strictly related to the geometry induced by the considered divergence on a regular parametric model. In particular, the existence of a global optimal predictive distribution is guaranteed for invariant divergences, whose local behaviour is similar to well known $\alpha $-divergences.

We show that a wide class of divergences obtained from weighted scoring rules share invariance properties with $\alpha $-divergences. For weighted scoring rules it is thus possible to obtain a global solution to the prediction problem. Unfortunately, the divergences associated to many widely used scoring rules are not invariant. Still for these cases we provide a locally optimal predictive distribution, within a specified parametric model.