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A Simulation Study on Generalized Pareto Mixture Model

  • Mustafa CavusEmail author
  • Ahmet Sezer
  • Berna Yazici
Chapter
  • 739 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 343)

Abstract

The Generalized Pareto Distribution is commonly used for extreme value problems. Especially, the values which exceed the finite threshold, is the focus in extreme value problems like in insurance sector. The Generalized Pareto Distribution is well approach for modeling the samples which include these extreme values. In the real life, samples are heterogeneous. In such cases, the mixture models are better way for modeling the data. In this study, we generate random samples from the Generalized Pareto Mixture Distribution for modeling of heterogeneous data. For this purpose, we use two different Generalized Pareto Distribution as components of the Generalized Pareto Mixture Distribution. For generating random samples, The Inverse Transformation Method is used in the simulation study. The parameters of the mixture models are shape, scale and location are fixed. After generating random samples, Chi-Square Goodness-of-Fit Test is used for checking whether the generated samples are distributed based on the Generalized Pareto Distribution. R-Statistical Programming Language is used in simulation study.

Keywords

The Generalized Pareto Mixture Distribution Mixture models The Inverse Transformation Method Chi-Square Goodness-of-Fit Test Generating random samples Pareto Distribution 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of StatisticsAnadolu UniversityEskisehirTurkey

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