Our paper, written jointly also with Anne-Laure Fougères, Christian Genest and Johanna Nešlehová, entitled Multivariate Archimax Copulas, should appear some day in the Journal of Multivariate Analysis.
“A multivariate extension of the bivariate class of Archimax copulas was recently proposed by Mesiar & Jagr (2013), who asked under which conditions it holds. This paper answers their question and provides a stochastic representation of multivariate Archimax copulas. A few basic properties of these copulas are explored, including their minimum and maximum domains of attraction. Several non-trivial examples of multivariate Archimax copulas are also provided.“
In this paper, we extend the class of Archimax copulas, introduced in dimension 2 in Bivariate Distributions with Given Extreme Value Attractor, by Philippe Capéraà, Anne-Laure Fougères and Christian Genest, inspired by some ideas mentioned in a paper published in Kybernetika a few years ago. I will try to post additional material, soon…
A common idea in statistics is that if we don’t know something, and we use anestimator of that something (instead of the true value) then there will be some additional uncertainty. For instance, consider a random sample, i.i.d., from a Gaussian distribution. Then, a confidence interval for the mean is
is the quantile of probability level
of the standard normal distribution
. But usually, standard deviation
is was talking about earlier) is usually unknown. So we substitute an estimation of the standard deviation, e.g.
and the cost we have to pay is that the new confidence interval is
where now is the quantile of the Student distribution, of probability level , with degrees of freedom.
We call it a cost since the new confidence interval is now larger (the Student distribution has higher upper-quantiles than the Gaussian distribution).
So usually, if we substitute an estimation to the true value, there is a price to pay.
A few years ago, with Jean David Fermanian and Olivier Scaillet, we were writing a survey on copula density estimation (using kernels, here). At the end, we wanted to add a small paragraph on the fact that we assumed that we wanted to fit a copula on a sample i.i.d. with distribution , a copula, but in practice, we start from a sample with joint distribution (assumed to have continuous margins, and – unique – copula ). But since margins are usually unknown, there should be a price for not observing them.
To be more formal, in a perfect wold, we would consider
but in the real world, we have to consider
where it is standard to consider ranks, i.e. are empirical cumulative distribution functions.
My point is that when I ran simulations for the survey (the idea was more to give illustrations of several techniques of estimation, rather than proofs of technical theorems) we observed that the price to pay… was negative ! I.e. the variance of the estimator of the density (wherever on the unit square) was smaller on the pseudo sample than on perfect sample .
By that time, we could not understand why we got that counter-intuitive result: even if we do know the true distribution, it is better not to use it, and to use instead a nonparametric estimator. Our interpretation was based on the discrepancy concept and was related to the latin hypercube construction:
With ranks, the data are more regular, and marginal distributions are exactlyuniform on the unit interval. So there is less variance.
This was our heuristic interpretation.
A couple of weeks ago, Christian Genest and Johan Segers proved that intuition in an article published in JMVA,
Well, we observed something for finite , but Christian and Johan obtained an analytical result. Hence, if we denote
the empirical copula in the perfect world (with known margins) and
the one constructed from the pseudo sample, they obtained that, everywhere
with nice graphs of ,
So I was very happy last week when Christian show me their results, to learn that our intuition was correct. Nevertheless, it is still a very counter-intuitive result…. If anyone has seen similar things, I’d be glad to hear about it !