Jean-Paul Benzécri (1932-2019)

I learned last weekend that Jean-Paul Benzécri had died earlier in the week. He was a leading and charismatic figure of the French renewal in data analysis (or analyse des données) that used mostly algebraic tools to analyse large datasets, while staying as far as possible from the strong abstraction of French statistics at that time. While I did not know him on a personal basis, I remember from my lecturer years there that he used to come to Institut de Statistique de l’Université de Paris (ISUP), Université Pierre et Marie Curie, once a week and meet with a large group of younger statisticians, students and junior faculty, and then talk to them for long hours while walking back and forth along the corridor in Jussieu. Showing extreme dedication from the group as this windowless corridor was particularly ghastly! (I also remember less fondly hours spent over piles and piles of SAS printout trying to make sense of multiple graphs of projections produced by these algebraic methods and feeling there were too many degrees of freedom for them to feel rigorous enough.)

Christian Robert is giving a talk in Jussieu tomorrow

My namesake Christian (Yann) Robert (CREST) is giving a seminar tomorrow in Jussieu (Université Pierre & Marie Curie, couloir 16-26, salle 209), between 2 and 3, on composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions. Here is the abstract:

We consider the family of hierarchical Archimedean copulas obtained from multivariate exponential mixture distributions through compounding, as introduced by Cossette et al. (2017). We investigate ways of determining the structure of these copulas and estimating their parameters. An agglomerative clustering technique based on the matrix of Spearman’s rhos, combined with a bootstrap procedure, is used to identify the tree structure. Parameters are estimated through a top-down composite likelihood. The validity of the approach is illustrated through two simulation studies in which the procedure is explained step by step. The composite likelihood method is also compared to the full likelihood method in a simple case where the latter is computable.

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Journée algorithmes stochastiques

On December 1, 2017, we will hold a day workshop on stochastic algorithms at Université Paris-Dauphine, with the following speakers

• Rémi Bardenet – CNRS Lille / CRISTAL [10:00]
• Nicolas Chopin – ENSAE / CREST [11:00]
• Aymeric Dieuleveut – ENS / DI & INRIA [14:00]
• Aude Genevay – Dauphine / CEREMADE & INRIA [15:00]
• Pierre Monmarché – UPMC / LJLL [16:30]

 Details and abstracts of the talks are available on the workshop webpage. Attendance is free, but registration is requested towards planning the morning and afternoon coffee breaks. Looking forward seeing ‘Og’s readers there, at least those in the vicinity!

And while I am targetting Parisians, crypto-Bayesians, and nearly-Parisians, there is another day workshop on Bayesian and PAC-Bayesian methods on November 16, at Université Pierre et Marie Curie (campus Jussieu), with invited speakers

• Arnak Dalalyan
• Alain Durmus
• Peter Grunwald
• Stéphanie van der Pas
• James Ridgway
• Elodie Vernet

and a similar request for (free) registration.

It’s the selection’s fault not the p-values’… [seminar]

Yoav Benjamini will give a seminar talk in Paris next Monday on the above (full title: “The replicability crisis in science: It’s the selection’s fault not the p-values’“). (That I will miss for being in Warwick at the time.) With a fairly terse abstract:

I shall discuss the problem of lack of replicability of results in science, and point at selective inference as a statistical root cause. I shall then present a few strategies for addressing selective inference, and their application in genomics, brain research and earlier phases of clinical trials where both primary and secondary endpoints are being used.

Details: February 8, 2016, 16h, Université Pierre & Marie Curie, campus Jussieu, salle 15-16-101.

Paris Machine Learning Meeting #10 Season 2

Tonight, I am invited to give a speed-presenting talk at the Paris Machine Learning last meeting of Season 2, with the themes of DL, Recovering Robots, Vowpal Wabbit, Predcsis, Matlab, and Bayesian test [by yours truly!] The meeting will take place in Jussieu, Amphi 25, Here are my slides for the meeting:

As it happened, the meeting  was quite crowded with talks and plagued with technical difficulties in transmitting talks from Berlin and Toronto, so I came to talk about three hours after the beginning, which was less than optimal for the most technical presentation of the evening. I actually wonder if I even managed to carry the main idea of replacing Bayes factors with posteriors of the mixture weight! [I had plenty of time to reflect upon this on my way back home as I had to wait for several and rare and crowded RER trains until one had enough room for me and my bike!]