Archive for Rutgers University

Tierras Centro Americanas [journal of the NYC weekend]

Posted in Books, Kids, pictures, Travel with tags , , , , , , , , , , , , , , , , , , on April 27, 2022 by xi'an

Upon my arrival at JFK, Queens, Andrew took me to have unbelievable tortillas in this Guatemaltec restaurant, soft and yummy, almost like pancakes! Along with great food altogether. We also had a pleasant stroll walking through Queens’ lively Jamaica district. Including coming upon a just extinguished fire in a row of shops! On the opposite, I did not see much of New Brunswick, apart from walking by the Harvest Moon brewery where I got a beer (and a tee-shirt) on my earlier visit there.

Read Truthwitch, another disappointment in the series (of recent books), as the universe building could have been great (despite being heavily inspired from Western Europe geography and culture, and in particular of Venezia. Again, I presume I was missing the YA label when I first picked this book! Scenario is rather terrible, full of last second rescues, new and convenient forms of magical powers, while interactions about characters are artificial and predictable, definitely not recommended. (And there are five books in the series!)

Watched The Silent Sea a short Korean TV serie taking place mostly in a Korean infected base on the Moon. While trying to solve the water crisis on a drying Earth (looking red from the Moon). The ending is quite disappointing while the original idea was most appealing. The science (fiction) behind the story is however terrible. (E.g., never use guns in space! And why would astronauts rely on cheap, handheld, lamplights to explore dark tunnels?! And how can you hide stealthy visits to a Moon basis from Earth?! &tc.)

Bill’s 80th!!!

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , on April 17, 2022 by xi'an

“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

Posted in Statistics, Travel, University life with tags , , , , , , , , , , on March 30, 2022 by xi'an

estimation of a normal mean matrix

Posted in Statistics with tags , , , , , , , , , on May 13, 2021 by xi'an

A few days ago, I noticed the paper Estimation under matrix quadratic loss and matrix superharmonicity by Takeru Matsuda and my friend Bill Strawderman had appeared in Biometrika. (Disclaimer: I was not involved in handling the submission!) This is a “classical” shrinkage estimation problem in that covariance matrix estimators are compared under under a quadratic loss, using Charles Stein’s technique of unbiased estimation of the risk is derived. The authors show that the Efron–Morris estimator is minimax. They also introduce superharmonicity for matrix-variate functions towards showing that generalized Bayes estimator with respect to a matrix superharmonic priors are minimax., including a generalization of Stein’s prior. Superharmonicity that relates to (much) earlier results by Ed George (1986), Mary-Ellen Bock (1988),  Dominique Fourdrinier, Bill Strawderman, and Marty Wells (1998). (All of whom I worked with in the 1980’s and 1990’s! in Rouen, Purdue, and Cornell). This paper also made me realise Dominique, Bill, and Marty had published a Springer book on Shrinkage estimators a few years ago and that I had missed it..!

Fisher, Bayes, and predictive Bayesian inference [seminar]

Posted in Statistics with tags , , , , , , , , , on April 4, 2021 by xi'an

An interesting Foundations of Probability seminar at Rutgers University this Monday, at 4:30ET, 8:30GMT, by Sandy Zabell (the password is Angelina’s birthdate):

R. A. Fisher is usually perceived to have been a staunch critic of the Bayesian approach to statistics, yet his last book (Statistical Methods and Scientific Inference, 1956) is much closer in spirit to the Bayesian approach than the frequentist theories of Neyman and Pearson.  This mismatch between perception and reality is best understood as an evolution in Fisher’s views over the course of his life.  In my talk I will discuss Fisher’s initial and harsh criticism of “inverse probability”, his subsequent advocacy of fiducial inference starting in 1930, and his admiration for Bayes expressed in his 1956 book.  Several of the examples Fisher discusses there are best understood when viewed against the backdrop of earlier controversies and antagonisms.

%d bloggers like this: