Improved inference for the generalized Pareto distribution under linear, power and exponential normalization

Title:

Improved inference for the generalized Pareto distribution under linear, power and exponential normalization

Creator:

Mohareb Khaled, Osama, Mohamed Barakat, Haroon, and Khalil Rakha, Nourhan

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:6aefe496-6e6f-4826-8da4-a236aab5b880
uuid:6aefe496-6e6f-4826-8da4-a236aab5b880
doi:10.14736/kyb-2022-6-0883

Subject:

generalized Pareto distribution, generalized extreme value distribution, method of moments, probability weighted moments, L-moments, and linear-power-exponential normalization

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

We discuss three estimation methods: the method of moments, probability weighted moments, and L-moments for the scale parameter and the extreme value index in the generalized Pareto distribution under linear normalization. Moreover, we adapt these methods to use for the generalized Pareto distribution under power and exponential normalizations. A simulation study is conducted to compare the three methods on the three models and determine which is the best, which turned out to be the probability weighted moments. A new computational technique for improving fitting quality is proposed and tested on two real-world data sets using the probability weighted moments. We looked back at various maximal data sets that had previously been addressed in the literature and for which the generalized extreme value distribution under linear normalization had failed to adequately explain them. We use the suggested procedure to find good fits.

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

883-902

Source:

Kybernetika | 2022 Volume:58 | Number:6

Harvested from:

CDK

Metadata only:

false