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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Also, the graphical method is illustrated by a real data example.
📌 3 Key Takeaways
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Objective & Hypothesis:We present a graphical tool that was primarily proposed by Michiels et al. [18] and later modified by Durante et al. [4].
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Key Findings & Data:We conduct a Monte Carlo simulation study to investigate its performance.
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Conclusion & Impact:Also, the graphical method is illustrated by a real data example.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Copula · Pickands function · Extreme value copula
Abstract
We present a graphical tool that was primarily proposed by Michiels et al. [18] and later modified by Durante et al. [4]. We also improve this method to select the better fit of the given data among some extreme value copulas based on the Pickands dependence function. We conduct a Monte Carlo simulation study to investigate its performance. Also, the graphical method is illustrated by a real data example.
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Cite This Article
Susam, S. O. (2022). A Graphical Tool for Extreme Value Copula Selection Based on the Pickands Dependence Function. Türkiye İstatistik Derneği İstatistik Dergisi, Volume: 14 Issue: 2. https://ajindex.com/articles/1562007