Výsledky vyhledávání - "Verster, Andréhette"

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A different approach for choosing a threshold in peaks over threshold

Autor: Verster, Andréhette, Raubenheimer, Lizanne

Abstract In Extreme Value methodology the choice of threshold plays an important role in efficient modelling of observations exceeding the threshold. The threshold must be chosen high enough to ensure an unbiased extreme value index but choosing the

Externí odkaz: http://arxiv.org/abs/2006.05748

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Bias Reduced Peaks over Threshold Tail Estimation

Autor: Beirlant, Jan, Maribe, Gaonyalelwe, Naveau, Philippe, Verster, Andrehette

In recent years several attempts have been made to extend tail modelling towards the modal part of the data. Frigessi et al. (2002) introduced dynamic mixtures of two components with a weight function {\pi} = {\pi}(x) smoothly connecting the bulk and

Externí odkaz: http://arxiv.org/abs/1810.01296

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Penalized bias reduction in extreme value estimation for censored Pareto-type data, and long-tailed insurance applications

Autor: Beirlant, Jan, Maribe, Gaonyalelwe, Verster, Andrehette

The subject of tail estimation for randomly censored data from a heavy tailed distribution receives growing attention, motivated by applications for instance in actuarial statistics. The bias of the available estimators of the extreme value index can

Externí odkaz: http://arxiv.org/abs/1705.06634

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Reducing MSE in estimation of heavy tails: a Bayesian approach

Autor: Maribe, Gaonyalelwe, Verster, Andréhette, Beirlant, Jan

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) under control, are especially useful when applying classical methods

Externí odkaz: http://arxiv.org/abs/1606.05687

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