Archive for Bill Strawderman

a (sunny, crisp) day at ICSDS 2025

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , on December 19, 2025 by xi'an

While my first day at ICSDS 2025 was somewhat hectic, having realised late the night before that I was giving a talk!—I had forgotten I had submitted a title at registration time and never received any communication from the organisers, including (or excluding) a request for an abstract. I thus hastily updated my November talk in Sevilla for my December talk in Sevilla! but paid less attention than needed to the sessions I attended—, Wednesday was more peaceful—esp. after a 16K run along the Guadalquivir—and I engaged into two great Bayesian learning sessions, one that seemed designed for me!, involving my (40y long friend) Ed George on his latest result on proper prior minimaxity and shrinkage, with our late friend Bill Strawderman as a co-author since they worked on the problem prior to Bill’s demise, Charles Margossian on variational inference preserving some symmetries in the target and hence keeping the same statistics, with elliptically symmetric families, and Fletcher Christensen on DIC for some mixed models, with references to our “DIC’s eights” paper (but still picking one version of DIC in the end!)

The second session was on prediction learning!—with me as the chair, as I realized one minute before! AI !—with (my friend) Veronika Rockova using AI predictions as a prior predictive and connecting them with Bayesian nonparametrics, Kenyon Ng (who visited me last Spring) on a similar approach using pretrained transformers like TabPFN and martingale posterior inference, Lorenzo Cappello in a generalisation of martingale prediction and Andrea Ghiglietti on the mathematics of an involved urn system.


The afternoon session was a plenary talk by Daniela Witten in the magnificent building of the Real Fabrica de Tabacos, but the room was unfortunately too small for the audience and I could not enter. Hopefully her talk will have a significant intersection with the CRiSM colloquium she delivers in Warwick late January. I thus walked around the old town till the following poster session, held in the Real Fabrica courtyard, under the sun. As I got involved into a deep discussion of the relevance of mirror meetings (which I defend!) versus the dangers on principal (parent) conferences (which can be mitigated by the mirror conference participants registering, to some extent, for the principle one)—more to come on the ‘Og and in the ISBA Bulletin!—, I did not peruse the available posters, sorry…

And, by the way, the conference organisers also revealed the location of ICSDS 2026 which is Croatia, my first bet! In the city of Split we visited in 2023.

William (Bill) Strawderman (1941-2024)

Posted in pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , on October 3, 2024 by xi'an

Earlier today, I was informed by several of our mutual friends that my long-time friend Bill Strawderman had sadly passed away yesterday, after fighting a cancer for the past months. I remember quite clearly meeting Bill in the Fall of 1988 in front of White Hall, which hosted the Cornell maths department at the time, as he was visiting George Casella from Rutgers where he spent most of his career. I was most eager to meet him as I had worked on several of his landmark papers during my PhD on shrinkage estimation, as well as a bit impressed. But his kindness, modesty, and congenial personality quickly put me at ease and we spent the rest of his visit discussing shrinkage but also literature and music. Especially Dickens! After that we met and collaborated quite regularly, to the point he started visiting France upon my return, at Paris 6 (Pierre & Marie Curie) University first, and then in Rouen, where he became a adjunct professor and launched a life-long collaboration and friendship with Dominique Fourdrinier. As my interest in shrinkage estimation dwindled along the years, we did not keep collaborating for the past two decades, but we remained in touch and I was very happy to participate in his 80th anniversary celebration in Rutgers two years ago. His contributions to the field are notable and several papers of his were part of the Bayesian classics I was giving my graduate class a few years ago. From the fabulous minimaxity paper of 1984, along with George Casella, to admissible estimators dominating the positive-part James-Stein estimator, to sufficient conditions of minimaxity for proper Bayes estimators, to decision theoretic properties of Bayesian credible interval estimators, to loss estimation, not to mention his more applied side… Besides his fabulous sense of humour, which made many evenings with him memorable, I will also cherish the memory of a bon vivant who liked good food and good wines, incl. the Calvados apple brandy I would bring him at each of my visits.

prior against truth!

Posted in Books, Kids, Statistics with tags , , , , , , , on June 4, 2018 by xi'an

A question from X validated had interesting ramifications, about what happens when the prior does not cover the true value of the parameter (assuming there ? In fact, not so much in that, from a decision theoretic perspective, the fact that that π(θ⁰)=0, or even that π(θ)=0 in a neighbourhood of θ⁰ does not matter [too much]. Indeed, the formal derivation of a Bayes estimator as minimising the posterior loss means that the resulting estimator may take values that were “impossible” from a prior perspective! Indeed, taking for example the posterior mean, the convex combination of all possible values of θ under π may well escape the support of π when this support is not convex. Of course, one could argue that estimators should further be restricted to be possible values of θ under π but that would reduce their decision theoretic efficiency.

An example is the brilliant minimaxity result by George Casella and Bill Strawderman from 1981: when estimating a Normal mean μ based on a single observation xwith the additional constraint that |μ|<ρ, and when ρ is small enough, ρ≤1.0567 quite specifically, the minimax estimator for this problem under squared error loss corresponds to a (least favourable) uniform prior on the pair {−ρ,ρ}, meaning that π gives equal weight to −ρ and ρ (and none to any other value of the mean μ). When ρ increases above this bound, the least favourable prior sees its support growing one point at a time, but remaining a finite set of possible values. However the posterior expectation, 𝔼[μ|x], can take any value on (−ρ,ρ).

In an even broader suspension of belief (in the prior), it may be that the prior has such a restricted support that it cannot consistently estimate the (true value of the) parameter, but the associated estimator may remain admissible or minimax.

reading classics (#9,10)

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , , , on January 28, 2014 by xi'an

La Défense from Paris-Dauphine, Nov. 15, 2012Today was the very last session of our Reading Classics Seminar for the academic year 2013-2014. We listened two presentations, one on the Casella and Strawderman (1984) paper on the estimation of the normal bounded mean. And one on the Hartigan and Wong’s 1979 K-Means Clustering Algorithm paper in JRSS C. The first presentation did not go well as my student had difficulties with the maths behind the paper. (As he did not come to ask me or others for help, it may well be that he put this talk together at the last minute, at a time busy with finals and project deliveries. He also failed to exploit those earlier presentations of the paper.) The innovative part in the talk was the presentation of several R simulations comparing the risk of the minimax Bayes estimator with the one for the MLE. Although the choice of simulating different samples of standard normals for different values of the parameters and even for both estimators made the curves (unnecessarily) all wiggly.

By contrast, the second presentation was very well-designed, with great Beamer slides, interactive features and a software oriented focus. My student Mouna Berrada started from the existing R function kmeans to explain the principles of the algorithm, recycling the interactive presentation of last year as well (with my permission), and creating a dynamic flowchart that was most helpful. So she made the best of this very short paper! Just (predictably) missing the question of the statistical model behind the procedure. During the discussion, I mused why k-medians clustering was not more popular as it offered higher robustness guarantees, albeit further away from a genuine statistical model. And why k-means clustering was not more systematically compared with mixture (EM) estimation.

Here are the slides for the second talk

[speakerdeck https://speakerdeck.com/xianblog/mouna-berradas-presentation-of-hartigan-and-wongs-1979-k-means-clustering-algorithm-paper-in-jrss-c%5D