Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk

TL;DR

This paper investigates the theoretical properties and convergence rates of distributional regression methods evaluated with the CRPS, establishing optimal minimax rates and analyzing specific methods like k-nearest neighbors and kernel approaches.

Contribution

It extends existing theoretical results on CRPS evaluation to covariate-dependent models and finite samples, identifying optimal convergence rates and demonstrating that certain methods attain these rates.

Findings

Optimal minimax convergence rate for distributional regression with CRPS.

k-nearest neighbor and kernel methods achieve the optimal rate.

Extended theoretical understanding of CRPS evaluation in covariate-dependent settings.

Abstract

The theoretical advances on the properties of scoring rules over the past decades have broadened the use of scoring rules in probabilistic forecasting. In meteorological forecasting, statistical postprocessing techniques are essential to improve the forecasts made by deterministic physical models. Numerous state-of-the-art statistical postprocessing techniques are based on distributional regression evaluated with the Continuous Ranked Probability Score (CRPS). However, theoretical properties of such evaluation with the CRPS have solely considered the unconditional framework (i.e. without covariates) and infinite sample sizes. We extend these results and study the rate of convergence in terms of CRPS of distributional regression methods. We find the optimal minimax rate of convergence for a given class of distributions and show that the k-nearest neighbor method and the kernel method reach this optimal minimax rate.