You don’t often see references to group theory in a statistics book. Not that there aren’t symmetries in statistics that could be described in terms of groups, but this isn’t often pointed out.

Here’s an example from An Introduction to Copulas by Roger Nelsen.

Show that under composition the set of operations of forming the survival copula, the dual of a copula, and the co-copula of a given copula, along with the identity (i.e., ^, ~, *, and *i*) yields the dihedral group.

Nelsen gives the following multiplication table for copula operations.

o | i ^ ~ * ----------- i | i ^ ~ * ^ | ^ i * ~ ~ | ~ * i ^ * | * ~ ^ i

The rest of this post explains what a copula is and what the operations above are.

## What is a copula?

At a high level, a copula is a mathematical device for modeling the dependence between random variables. Sklar’s theorem says you can express the joint distribution of a set of random variables in terms of their marginal distributions and a copula. If the distribution functions are continuous, the copula is unique.

The precise definition of a copula is technical. We’ll limit ourselves to copulas in two dimensions to make things a little simpler.

Let *I* be the unit interval [0, 1]. Then a (two-dimensional) **copula** is a function from *I* × *I* to *I* that satisfies

and is **2-increasing**.

The idea of a 2-increasing function is that “gradients point northeast.” Specifically, for all points (*x*_{1}, *y*_{1}) and (*x*_{2}, *y*_{2}) with *x*_{1} ≤ *x*_{2} and *y*_{1} ≤ *y*_{2}, we have

The definition of copula makes no mention of probability, but the 2-increasing condition says that *C* acts like the joint CDF of two random variables.

## Survival copula, dual copula, co-copula

For a given copula *C*, the corresponding survival copula, dual copula, and co-copula are defined by

respectively.

The reason for the name “survival” has to do with a survival function, i.e. complementary CDF of a random variable. The survival copula is another copula, but the dual copula and co-copulas aren’t actually copulas.

This post hasn’t said much too about motivation or application—that would take a lot more than a short blog post—but it has included enough that you could verify that the operations do compose as advertised.

**Update**: See this post for more algebraic structure for copulas, a sort of convolution product.