34 citations · 41 across the 7 of their papers we have counts for
6 papers · 1 filter
Trimmed extreme value estimators for censored heavy-tailed data
Martin Bladt, Hansjoerg Albrecher, Jan Beirlant
We consider estimation of the extreme value index and extreme quantiles for heavy-tailed data that are right-censored. We study a general procedure of removing low importance obser…
Tempered Pareto-type modelling using Weibull distributions
Jose Carlos Araujo Acuna, Hansjoerg Albrecher, Jan Beirlant
In various applications of heavy-tail modelling, the assumed Pareto behavior is tempered ultimately in the range of the largest data. In insurance applications, claim payments are…
Estimation of the extreme value index in a censorship framework: asymptotic and finite sample behaviour
Jan Beirlant, Julien Worms, Rym Worms
We revisit the estimation of the extreme value index for randomly censored data from a heavy tailed distribution. We introduce a new class of estimators which encompasses earlier p…
Reducing MSE in estimation of heavy tails: a Bayesian approach
Gaonyalelwe Maribe, Andréhette Verster, Jan Beirlant
Bias reduction in tail estimation has received considerable interest in extreme value analysis. Estimation methods that minimize the bias while keeping the mean squared error (MSE)…
Tail fitting for truncated and non-truncated Pareto-type distributions
Jan Beirlant, Isabel Fraga Alves, Ivette Gomes
Recently some papers, such as Aban, Meerschaert and Panorska (2006), Nuyts (2010) and Clark (2013), have drawn attention to possible truncation in Pareto tail modelling. Sometimes…
Second-order refined peaks-over-threshold modelling for heavy-tailed distributions
Jan Beirlant, Elisabeth Joossens, Johan Segers
Modelling excesses over a high threshold using the Pareto or generalized Pareto distribution (PD/GPD) is the most popular approach in extreme value statistics. This method typicall…