3 citations · 5 across the 9 of their papers we have counts for
12 papers
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…
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…
Statistical Prediction of Peaks Over a Threshold
S. A. Padoan, Stefano Rizzelli
In many applied fields it is desired to make predictions with the aim of assessing the plausibility of more severe events than those already recorded to safeguard against calamitie…
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…
Marginal expected shortfall inference under multivariate regular variation
Simone A. Padoan, Stefano Rizzelli, Matteo Schiavone
Marginal expected shortfall is unquestionably one of the most popular systemic risk measures. Studying its extreme behaviour is particularly relevant for risk protection against se…
Strong Convergence of Peaks Over a Threshold
Simone A. Padoan, Stefano Rizzelli
Extreme Value Theory plays an important role to provide approximation results for the extremes of a sequence of independent random variables when their distribution is unknown. An…
Empirical Bayes inference for the block maxima method
Simone A Padoan, Stefano Rizzelli
The block maxima method is one of the most popular approaches for extreme value analysis with independent and identically distributed observations in the domain of attraction of an…
Consistency of Bayesian Inference for Multivariate Max-Stable Distributions
Simone A. Padoan, Stefano Rizzelli
Predicting extreme events is important in many applications in risk analysis. The extreme-value theory suggests modelling extremes by max-stable distributions. The Bayesian approac…
Strong Convergence of Multivariate Maxima
Michael Falk, Simone A. Padoan, Stefano Rizzelli
It is well known and readily seen that the maximum of independent and uniformly on distributed random variables, suitably standardised, converges in total variation dis…
Multivariate Extremes Over a Random Number of Observations
Enkelejd Hashorva, Simone A. Padoan, Stefano Rizzelli
The classical multivariate extreme-value theory concerns the modeling of extremes in a multivariate random sample, suggesting the use of max-stable distributions. In this work, the…