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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The construction method is illustrated by three real data examples.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper, we are proposing a flexible method for constructing a bivariate generalized Farlie-Gumbel-Morgenstern (G-FGM) copula family.
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Key Findings & Data:The method is mainly developed around the function $\phi(t)$ ($t\in [0,1]$), where $\phi$ is the generator of the G-FGM copula.
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Conclusion & Impact:The construction method is illustrated by three real data examples.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bernstein polynomial · Copula · Kendall
Abstract
In this paper, we are proposing a flexible method for constructing a bivariate generalized Farlie-Gumbel-Morgenstern (G-FGM) copula family. The method is mainly developed around the function $\phi(t)$ ($t\in [0,1]$), where $\phi$ is the generator of the G-FGM copula. The proposed construction method has useful advantages. The first of which is the direct relationship between the $\phi$ function and Kendall's tau. The second advantage is the possibility of constructing a multi-parameter G-FGM copula which allows us to better harmonize empirical instruction with the model. The construction method is illustrated by three real data examples.
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Cite This Article
SUSAM, S. O. (2022). A multi-parameter Generalized Farlie-Gumbel-Morgenstern bivariate copula family via Bernstein polynomial. Hacettepe Journal of Mathematics and Statistics, Cilt: 51 - Sayı: 2, 618–631. https://ajindex.com/articles/669232