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I do understand the mathematic behind probability density function( PDF) and cumulative distribution function (CDF). My problem starts when I try to understand why copula relies on CDF and not on PDF. I searched and found this:

the probability density function can be hard to work with directly. What I found confused me again.

So,

1- Why copula relies on CDF and not PDf?

2- Why is it hard to work with PDF directly?

3- Why always CDF is used in integral transformation function. That is, why we cannot transform the variables into their uniform distribution using PDF?

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  • $\begingroup$ Sklar's theorem guarantees the existence of a copula C for a joint CDF and marginals CDFs. $\endgroup$ – user289381 Jul 19 at 10:08
  • $\begingroup$ @ping Thanks so much for your comment. So, why cdf is used instead of pdf. Is that because cdf is unique? $\endgroup$ – Maryam Jul 19 at 10:13
  • $\begingroup$ Sklar's theorem not only guarantees the existence but it also gives a simple way to estimate the copula from the CDF en.wikipedia.org/wiki/… If all marginal CDFs are continuous, C is unique. $\endgroup$ – user289381 Jul 19 at 10:17
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    $\begingroup$ What do you mean by "copula relies on CDF and not on PDF"? Not every distribution may have a pdf but beyond that those two ways of describing a distribution are mathematically equivalent. What description works better depends on the problem you would like to solve. The ubiquitous bivariate scatter plots of copulas are a good example of the pdf-view. $\endgroup$ – g g Jul 19 at 10:22
  • $\begingroup$ @ping Thanks again. But why cdf is good in this case? what is the problem if we use pdf instead? $\endgroup$ – Maryam Jul 19 at 10:24
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Sklar's theorem guarantees the existence of a copula $C$, given a joint CDF and marginal CDFs.

The theorem gives an easy method to determine the copula $C$.

Why do we use CDF instead of PDF?
"Every random variable has a CDF. Not every random variable has a PDF"

https://math.stackexchange.com/questions/3473846/why-is-there-a-preference-to-use-the-cumulative-distribution-function-to-charact

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