Model diagnostics and forecast evaluation are closely related tasks, with the former
concerning in-sample goodness (or lack) of fit and the latter addressing predictive …

Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …

Probabilistic solar forecasts may take the form of predictive probability distributions,
ensembles, quantiles, or interval forecasts. State-of-the-art approaches build on input from …

Elicitability and backtesting: Perspectives for banking regulation Page 1 The Annals of Applied
Statistics 2017, Vol. 11, No. 4, 1833–1874 https://doi.org/10.1214/17-AOAS1041 © Institute of …

Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …

This open access book discusses the statistical modeling of insurance problems, a process
which comprises data collection, data analysis and statistical model building to forecast …

A probability forecast or probabilistic classifier is reliable or calibrated if the predicted
probabilities are matched by ex post observed frequencies, as examined visually in …

We introduce a very general method for high dimensional classification, based on careful
combination of the results of applying an arbitrary base classifier to random projections of …

In public discussions of the quality of forecasts, attention typically focuses on the predictive
performance in cases of extreme events. However, the restriction of conventional forecast …

We use tail expectiles to estimate alternative measures to the value at risk and marginal
expected shortfall, which are two instruments of risk protection of utmost importance in …