[Published] Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk
Published in International Journal of Forecasting, 2022
Recommended citation: Pic, Romain, Dombry, Clément, Naveau, Philippe, & Taillardat, Maxime (2022). "Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk". International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2022.11.001
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.
Article : 10.1016/j.ijforecast.2022.11.001
Talks and posters associated to the article
- [Poster] EGU NP5.1 session, Vienna, Austria
- Forecast Verification and Data Assimilation workshop, Reading, UK
- Reading Group of WMO Joint Working Group on Forecast Verification Research, Online
- 53e Journées de Statistique, Lyon, FR
- Machine Learning and Uncertainties in Climate Simulations, Moulin-Mer, FR
- Forum des Jeunes Mathématicien.ne.s 2021, Besançon, FR