I am currently in Leuven for a few days. It is always a pleasure to be back to the place where I defended my PhD, a few years ago.
I will give a talk, tomorrow, at noon, on nonparametric (and kernel related) inference for quantiles and risk measures, inspired by recent work with Emmanuel Flachaire. Our first paper log-transform kernel density estimationof income distribution is online on http://papers.ssrn.com/id=2514882, and should appear soon. Another one it able to be finalised, soon.
Emmanuel Flachaire will be presenting some joint work in Càdiz, Spain, tomorrow evening, at the second conference of the International Society of NonParametric Statistics. Jeff invited me a few months ago, to go there, but unfortunately, I’ve already been moving a lot recently. The talk will be based on the same work that I mentioned at the SSC annual conference (Canadian Statistical Society), in Toronto, at the end of May. His talk is on quantiles and inequality indices estimation from heavy-tailed distribution. As mentioned in my previous post, we will upload the slides (and the paper) in a close future.
So, Emmanuel will go there, and enjoy the beach (and the conference, the program is truly amazing).
I will be in Toronto this week, for the SSC annual conference (Canadian Statistical Society), to present some recent work, with Emmanuel Flachaire. Because of some administrative duty last week, I am a bit late, so I won’t be able to upload the slides before the talk. Sorry about that…
More to come when I’ll be back in Montréal !
Le papier sur l’estimation de quantile par noyau beta, coécrit avec Abder Oulidi, est accepté pour publication dans Statistics and Computing, http://link.springer.com/…
In this paper we propose several nonparametric estimators of quantiles based on Beta kernel and applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte-Carlo based study will show that those estimators improve the efficiency of a traditional ones, not only for light tailed distributions, but also heavy tails, when the probability level is close to 1. We also compare these estimators with the Extreme Value Theory Quantile applying to Danish data on large fire insurance losses.