Category Archives: Trip

Niort, Data Day

Lundi et mardi, je serais à Niort où la MAIF organise un Data Day avec plusieurs conférences (tables rondes) autour de la donnée

L’arrivée du digital créée de nouvelles opportunités, de nouveaux besoins, et de nouveaux risques. Dans ce contexte fortement évolutif et très complexe comment les personnes que nous sommes vont-ils évoluer autour de l’information de son usage et de sa gouvernance ? Quelles tendances peut-on observer ? Quelles compétences à quels horizons et pour quelles attentes ?

On my way to Bruxelles, Belgium

After Amsterdam, the next stop is Bruxelles, Belgium. Here again, it is always a pleasure to get there… And then, time to get back home, to finalize some posts, prepare the new lectures, and answer all the late emails I got during those past days…

(illustration par l’immense Yves Chaland, français certes, mais qui a su relancer la ligne claire quand j’étais plus jeune, dans l’esprit des grands belges, comme Franquin…  et le Jeune Albert se passe à Bruxelles, alors on va dire que je peux légitimement utiliser un de ses dessins pour illustrer un voyage à Bruxelles).

Probit Transformation for Nonparametric Kernel Estimation of the Copula Density, Lille

This Monday I will be in Lille to give a talk at the Journées de Statistiques. The talk will be based on the joint work with Gery Geenens and Davy Paindaveine, on Probit transformation for nonparametric kernel estimation of the copula density”. The papier can be found online, on http://arxiv.org/abs/1404.4414

Copula modelling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametric density estimator, the kernel estimator is not suitable for the unit-square-supported copula densities, mainly because it is heavily affected by boundary bias issues. In addition, most common copulas admit unbounded densities, and kernel methods are not consistent in that case. In this paper, a kernel-type copula density estimator is proposed. It is based on the idea of transforming the uniform marginals of the copula density into normal distributions via the probit function, estimating the density in the transformed domain, which can be accomplished without boundary problems, and obtaining an estimate of the copula density through back-transformation. Although natural, a raw application of this procedure was, however, seen not to perform very well in the earlier literature. Here, it is shown that, if combined with local likelihood density estimation methods, the idea yields very good and easy to implement estimators, fixing boundary issues in a natural way and able to cope with unbounded copula densities. The asymptotic properties of the suggested estimators are derived, and a practical way of selecting the crucially important smoothing parameters is devised. Finally, extensive simulation studies and a real data analysis evidence their excellent performance compared to their main competitors.”

The slides are available on Dropbox (it is a 54Mo file with animated pictures, that do not appear on the version below).