Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Beta kernel estimators for density functions
Computational Statistics & Data Analysis
Using a bootstrap method to choose the sample fraction in tail index estimation
Journal of Multivariate Analysis
Weighted quantile-based estimation for a class of transformation distributions
Computational Statistics & Data Analysis
Estimation of upper quantiles under model and parameter uncertainty
Computational Statistics & Data Analysis
Short Communication: On quantile estimation by bootstrap
Computational Statistics & Data Analysis
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In this paper we suggest several nonparametric quantile estimators based on Beta kernel. They are applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte Carlo based study has shown that those estimators improve the efficiency of the traditional ones, not only for light tailed distributions, but also for heavy tailed, when the probability level is close to 1. We also compare these estimators with the Extreme Value Theory Quantile applied to Danish data on large fire insurance losses.