4 citations · 8 across the 3 of their papers we have counts for
3 papers
stat.AP2019★ 3 cited
Generalizing the log-Moyal distribution and regression models for heavy tailed loss data
Zhengxiao Li, Jan Beirlant, Shengwang Meng
Catastrophic loss data are known to be heavy-tailed. Practitioners then need models that are able to capture both tail and modal parts of claim data. To this purpose, a new paramet…
stat.ME2019★ 1 cited
Outlier detection and a tail-adjusted boxplot based on extreme value theory
Shrijita Bhattacharya, Jan Beirlant
Whether an extreme observation is an outlier or not, depends strongly on the corresponding tail behaviour of the underlying distribution. We develop an automatic, data-driven metho…
math.ST2014★ 4 cited
Extreme value statistics for truncated Pareto-type distributions
Jan Beirlant, Isabel Fraga Alves, Ivette Gomes +1
Recently attention has been drawn to practical problems with the use of unbounded Pareto distributions, for instance when there are natural upper bounds that truncate the probabili…