5 papers
Empirical Bayes in Bayesian learning: understanding a common practice
Stefano Rizzelli, Judith Rousseau, Sonia Petrone
In applications of Bayesian procedures, once a class of priors has been chosen, it may be tempting to fix the prior's hyperparameters from the data, in an empirical Bayes (EB) fash…
Accurate Bayesian inference for tail risk extrapolation in time series
David L. Carl, Simone A. Padoan, Stefano Rizzelli
Accurately quantifying tail risks-rare but high-impact events such as financial crashes or extreme weather-is a central challenge in risk management, with serially dependent data.…
Asymptotic theory for the likelihood-based block maxima method in time series
David L. Carl, Simone A. Padoan, Stefano Rizzelli
This paper develops a rigorous asymptotic framework for likelihood-based inference in the Block Maxima (BM) method for stationary time series. While Bayesian inference under the BM…
Statistical Prediction of Peaks Over a Threshold
Simone A. Padoan, Stefano Rizzelli
In many applied fields, the prediction of more severe events than those already recorded is crucial for safeguarding against potential future calamities. What-if analyses, which ev…
Asymptotic theory for Bayesian inference and prediction: from the ordinary to a conditional Peaks-Over-Threshold method
Clément Dombry, Simone A. Padoan, Stefano Rizzelli
The Peaks Over Threshold (POT) method is the most popular statistical method for the analysis of univariate extremes. Even though there is a rich applied literature on Bayesian inf…