Bill’s 80th!!!

“It was the best of times,
it was the worst of times”
[Dickens’ Tale of Two Cities (which plays a role in my friendship with Bill!)]

My flight to NYC last week was uneventful and rather fast and I worked rather well, even though the seat in front of me was inclined to the max for the entire flight! (Still got glimpses of Aline and of Deepwater Horizon from my neighbours.) Taking a very early flight from Paris was great making a full day once in NYC,  but “forcing” me to take a taxi, which almost ended up in disaster since the Über driver did not show up. At all. And never replied to my message. Fortunately trains were running, I was also running despite the broken rib, and I arrived at the airport some time before access was closed, grateful for the low activity that day. I also had another bit of a worrying moment at the US border control in JFK as I ended up in a back-office of the Border Police after the machine could not catch my fingerprints. And another stop at the luggage control as my lack of luggage sounded suspicious!The conference was delightful in celebrating Bill’s carreer and kindness (tinted with the most gentle irony!). Among stories told at the banquet, I was surprised to learn of Bill’s jazz career side, as I had never heard him play the piano or the clarinet! Even though we had chatted about music and literature on many occasions. Since our meeting in 1989… The (scientific side of the) conference included many talks around shrinkage, from loss estimation to predictive estimation, reminding me of the roaring 70’s and 80’s [James-Stein wise]. And demonstrating the impact of Bill’s wor throughout this era (incl. on my own PhD thesis). I started wondering at the (Bayesian) use of the loss estimate, though, as I set myself facing two point estimators attached with two estimators of their loss: it did not seem a particularly good idea to systematically pick the one with the smallest estimate (and Jim Berger confirmed this feeling on a later discussion). Among the talks on less familiar topics (of mine), I discovered work of Genevera Allen‘s on inferring massive network for neuron connections under sparse information. And of Emma Jingfei Zhang, equally centred on network inference, with applications to brain connectivity.

In a somewhat remote connection with Bill’s work (and our joint and hilarious assessment of Pitman closeness), I presented part of our joint and current work with Adrien Hairault and Judith Rousseau on inferring the number of components in a mixture by Bayes factors when the alternative is an infinite mixture (i.e., a Dirichlet process mixture). Of which Ruobin Gong gave a terrific discussion. (With a connection to her current work on Sense and Sensitivity.)

I was most sorry to miss Larry Wasserman’s and Rob Strawderman’s talk to rush back to the airport, the more because I am sure Larry’s talk would have brought a new light on causality (possibly equating it with tequila and mixtures!). The flight back was uneventfull, the plane rather empty and I slept most of the time. Overall,  it was most wonderful to re-connect with so many friends. Most of whom I had not seen for ages, even before the pandemic. And to meet new friends. (Nothing original in the reported feeling, just telling that the break in conferences and workshops was primarily a hatchet job on social relations and friendships.)

Bill’s 80th birthday

same risk, different estimators

An interesting question on X validated reminded me of the epiphany I had some twenty years ago when reading a Annals of Statistics paper by Anirban Das Gupta and Bill Strawderman on shrinkage estimators, namely that some estimators shared the same risk function, meaning their integrated loss was the same for all values of the parameter. As indicated in this question, Stefan‘s instructor seems to believe that two estimators having the same risk function must be a.s. identical. Which is not true as exemplified by the James-Stein (1960) estimator with scale 2(p-2), which has constant risk p, just like the maximum likelihood estimator. I presume the confusion stemmed from the concept of completeness, where having a function with constant expectation under all values of the parameter implies that this function is constant. But, for loss functions, the concept does not apply since the loss depends both on the observation (that is complete in a Normal model) and on the parameter.

…and from Rutgers

After my seminar in Princeton, I went to Rutgers University, in New Brunwick, New Jersey, to meet my friend Bill Strawderman and my former PhD student Aude Grelaud, and spent a pleasant evening with them. The next day, after a quick tour of the historical campus (great Old Dutch buildings!), I had a series of meetings with faculty members and with students, where we discussed extensions and applications of ABC. The seminar was on a tighter schedule than in Princeton, but we also managed to discuss the selection of summary statistics, while I insisted more on the (precision) gain brought by a reduction in the dimension of those summary statistics.

The schedule was tight as I had to catch a plane to Paris in New York (JFK) the same evening but taking advantage of the fairly efficient train facilities around New York, we still managed to share a quick beer at the Harvest Moon Brewery Café (I wish I had had time to get a tee-shirt from there!)… (The rest of the trip was 100% uneventful as I managed to sleep the whole flight back home!)

Festschrift for William E. Strawderman

Éric Marchand just sent me the news that the Festschrift volume he edited jointly with Dominique Fourdrinier and Andrew Rukhin in honour of our dear friend Bill Strawderman has now appeared on ProjectEuclid. It is freely accessible, thanks to this great IMS policy of making everything available on-line. As my research focus drifted away from shrinkage estimation and decision theory, I am sorry I could not contribute to the volume… (I “met” Bill through my readings during my PhD thesis and “in the flesh” when visiting Cornell two years later. He has produced some of the most elegant results in the area of Stein estimation, incl. the one that no proper Bayes estimator can be minimax in dimension four or less, and if a particular shrinkage estimator was to be “Hall-of-Fame-d”, it would be his! Bill visited us in Rouen (Dominique and I) many times and even learned French in order to teach there. I also happened to have the most hilarious moment of my life [so far!] with him and George Casella on an Ithaca country road, late on a summer night, but I cannot alas disclose the details!!!)