Calibration of probabilistic forecasts

Proper scoring rules, skill scores etc

June 16, 2015 — November 15, 2023

model selection

regression

signal processing

statistics

stochastic processes

time series

Intuitively speaking, we need to ensure that if our prediction is 80% certain, we are wrong as close to 20% of the time as possible. The same applies to all other certainties.

Placeholder.

I do not know much about this, but I could probably start from the compact lit review in Gneiting and Raftery (2007).

1 References

Gneiting, and Raftery. 2007. “Strictly Proper Scoring Rules, Prediction, and Estimation.” Journal of the American Statistical Association.

Henzi, Shen, Law, et al. 2023. “Invariant Probabilistic Prediction.”

Pacchiardi, and Dutta. 2022. “Generalized Bayesian Likelihood-Free Inference Using Scoring Rules Estimators.” arXiv:2104.03889 [Stat].

Székely, and Rizzo. 2013. “Energy Statistics: A Class of Statistics Based on Distances.” Journal of Statistical Planning and Inference.