Copulas: Understanding U & V-P(U≤u)=C(u,1)=u?

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SUMMARY

The discussion focuses on the properties of copulas, specifically the relationship between the copula function C(u,v) and the probability P(U≤u). It establishes that P(U≤u) is equal to C(u,1) and clarifies that while C(u,∞) approaches P(U≤u) as v approaches infinity, it does not equal u directly. The participants confirm that copulas are defined on the domain [0,1]x[0,1], which is crucial for understanding their behavior in probability theory.

PREREQUISITES
  • Understanding of copulas in probability theory
  • Familiarity with joint distribution functions
  • Knowledge of limits in mathematical analysis
  • Basic concepts of probability, particularly cumulative distribution functions
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  • Study the properties of copulas in depth, focusing on their applications in statistics
  • Learn about joint distribution functions and their significance in probability theory
  • Explore the concept of limits and their role in defining copulas
  • Investigate the implications of copulas being defined on the domain [0,1]x[0,1]
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Statisticians, data scientists, and researchers in fields involving probability theory and joint distributions will benefit from this discussion.

iomtt6076
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I'd appreciate it if someone could help clear up something I'm not understanding in a textbook I'm studying:

Given a copula C(u,v), we have [tex]P(U\leq u)=C(u,1)=u[/tex].

But why isn't it [tex]P(U\leq u)=C(u,\infty)=u[/tex]? Isn't it true that [tex]C_U(u)=P(U\leq u)=\lim_{v\to\infty}C(u,v)[/tex]?
 
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iomtt6076 said:
But why isn't it [tex]P(U\leq u)=C(u,\infty)=u[/tex]? Isn't it true that [tex]C_U(u)=P(U\leq u)=\lim_{v\to\infty}C(u,v)[/tex]?

Yes it is, since copulas are also joint distribution functions.
 
Okay, thanks; I guess the textbook should have explicitly said copulas were defined on [0,1]x[0,1].
 

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