I was (exceptionally) working in (and for) my garden when my daughter shouted down from her window that John Nash had just died. I thus completed my tree trimming and went to check about this sad item of news. What I read made the news even sadder as he and his wife had died in a taxi crash in New Jersey, apparently for not wearing seat-belts, a strategy you would think far from minimax… Since Nash was in Norway a few days earlier to receive the 2015 Abel Prize, it may even be that the couple was on its way home back from the airport. A senseless death for a Beautiful Mind.
Archive for Nobel Prize
On December 4-5, Université Paris-Dauphine will host the 6th French Econometric Conference, which celebrates Christian Gouriéroux and his contributions to econometrics. (Christian was my statistics professor during my graduate years at ENSAE and then Head of CREST when I joined this research unit, first as a PhD student and later as Head of the statistics group. And he has always been a tremendous support for me.)
Not only is the program quite impressive, with co-authors of Christian Gouriéroux and a few Nobel laureates (if not the latest, Jean Tirole, who taught economics at ENSAE when I was a student there), but registration is free. I will most definitely attend the talks, as I am in Paris-Dauphine at this time of year (the week before NIPS). In particular, looking forward to Gallant’s views on Bayesian statistics.
Xiao-Li Meng asked this question in his latest XL column, to which Andrew replied faster than I. And in the same mood as mine. I had taken part to a recent discussion on this topic within the IMS Council, namely whether or not the IMS should associate with other organisations like ASA towards funding and supporting this potential prize. My initial reaction was one of surprise that we could consider mimicking/hijacking the Nobel for our field. First, I dislike the whole spirit of most prizes, from the personalisation to the media frenzy and distortion, to the notion that we could rank discoveries and research careers within a whole field. And separate what is clearly due to a single individual from what is due to a team of researchers.
Being clueless about those fields, I will not get into a discussion of who should have gotten a Nobel Prize in medicine, physics, or chemistry. And who should not have. But there are certainly many worthy competitors to the actual winners. And this is not the point: I do not see how any of this fights the downfall of scientific students in most of the Western World. That is, how a teenager can get more enticed to undertake maths or physics studies because she saw a couple old guys wearing weird clothes getting a medal and a check in Sweden. I have no actual data, but could Xiao-Li give me a quantitative assessment of the fact that Nobel Prizes “attract future talent”? Chemistry departments keep closing for lack of a sufficient number of students, (pure) maths and physics departments threatened with the same fate… Even the Fields Medal, which has at least the appeal of being delivered to younger researchers, does not seem to fit Xiao-Li’s argument. (To take a specific example: The recent Fields medallist Cédric Villani is a great communicator and took advantage of his medal to promote maths throughout France, in conferences, the medias, and by launching all kinds of initiative. I still remain sceptical about the overall impact on recruiting young blood in maths programs [again with no data to back up my feeling).) I will even less mention Nobel prizes for literature and peace, as there clearly is a political agenda in the nomination. (And selecting Sartre for the Nobel prize for literature definitely discredited it. At least for me.)
“…the media and public have given much more attention to the Fields Medal than to the COPSS Award, even though the former has hardly been about direct or even indirect impact on everyday life.” XL
Well, I do not see this other point of Xiao-Li’s. Nobel prizes are not prestigious for their impact on society, as most people do not understand at all what the rewarded research (career) is about. The most extreme example is the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel: On the one hand, Xiao-Li is right in pointing out that this is a very successful post-Alfred creation of a “Nobel Prize”. On the other hand, the fact that some years see two competing theories simultaneously win leads me to consider that this prize gives priority to theoretical construct above any impact on the World’s economy. Obviously, this statement is a bit of shooting our field in the foot since the only statisticians who got a Nobel Prize are econometricians and game-theorists! Nonetheless, it also shows that the happy few statisticians who entered the Nobel Olympus did not bring a bonus to the field… I am thus remaining my usual pessimistic self on the impact of a whatever-company Prize in Statistical Sciences in Memory of Alfred Nobel.
Another remark is the opposition between the COPSS Award, which remains completely ignored by the media (despite a wealth of great nominees with various domains of achievements) and the Fields Medal (which is not ignored). This has been a curse of Statistics that has been discussed at large, namely the difficulty to separate what is math and what is outside math within the field. The Fields Medal is clearly very unlikely to nominate a statistician, even a highly theoretical statistician, as there will always be “sexier” maths results, i.e. corpora of work that will be seen as higher maths than, say, the invention of the Lasso or the creation of generalized linear models. So there is no hope to reach for an alternative Fields Medal with the same shine. Just like the Nobel Prize.
Other issues I could have mentioned, but for the length of the current rant, are the creation of rewards for solving a specific problem (as some found in Machine Learning), for involving multidisciplinary and multicountry research teams, and for reaching new orders of magnitude in processing large data problems.
While Nobel prizes never get close to mathematics, it sometimes happens they border statistics. It was the case two years ago with Chris Sims—soon to speak at Bayes 250 in Duke— winning the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (often shortened into the Nobel Prize in Economic Sciences). This year was also a borderline year since an econometrician, Lars Peter Hansen, got a third of the prize… Hansen is the current co-editor of Econometrica and one of the main contributors to the theory of the generalised method of moments. (Which can be re-interpreted as a precursor to ABC!) He has most of his papers in Econometrica, Journal of Econometrics, and other econom’ics journals, but also has a 2009 paper in the Annals of Statistics. In addition, even though the case is even more borderline, the fact that simulation techniques are at the core of the 2013 Nobel Prize in Chemistry is also a good thing for our field (although I did not see much mentions made of statistics in the reports I read, apart from their methods making “good predictions”…)
Another day full of interesting and challenging—in the sense they generated new questions for me—talks at the SuSTain workshop. After another (dry and fast) run around the Downs; Leo Held started the talks with one of my favourite topics, namely the theory of g-priors in generalized linear models. He did bring a new perspective on the subject, introducing the notion of a testing Bayes factor based on the residual statistic produced by a classical (maximum likelihood) analysis, connected with earlier works of Vale Johnson. While I did not truly get the motivation for switching from the original data to this less informative quantity, I find this perspective opening new questions for dealing with settings where the true data is replaced with one or several classical statistics. With possible strong connections to ABC, of course. Incidentally, Leo managed to produce a napkin with Peter Green’s intro to MCMC dating back from their first meeting in 1994: a feat I certainly could not reproduce (as I also met both Peter and Leo for the first time in 1994, at CIRM)… Then Richard Everit presented his recent JCGS paper on Bayesian inference on latent Markov random fields, centred on the issue that simulating the latent MRF involves an MCMC step that is not exact (as in our earlier ABC paper for Ising models with Aude Grelaud). I already discussed this paper in an earlier blog and the only additional question that comes to my mind is whether or not a comparison with the auxiliary variable approach of Møller et al. (2006) would make sense.
In the intermission, I had a great conversation with Oliver Ratman on his talk of yesterday on the surprising feature that some models produce as “data” some sample from a pseudo-posterior.. Opening once again new vistas! The following talks were more on the mathematical side, with James Cussens focussing on the use of integer programming for Bayesian variable selections, then Éric Moulines presenting a recent work with a PhD student of his on PAC-Bayesian bounds and the superiority of combining experts. Including a CRAN package. Éric concluded his talk with the funny occurence of Peter’s photograph on Éric’s Microsoft Research Profile own page, due to Éric posting our joint photograph at the top of Pic du Midi d’Ossau in 2005… (He concluded with a picture of the mountain that was the exact symmetry of mine yesterday!)
The afternoon was equally superb with Gareth Roberts covering fifteen years of scaling MCMC algorithms, from the mythical 0.234 figure to the optimal temperature decrease in simulated annealing, John Kent playing the outlier with an EM algorithm—however including a formal prior distribution and raising the challenge as to why Bayesians never had to constrain the posterior expectation, which prompted me to infer that (a) the prior distribution should include all constraints and (b) the posterior expectation was not the “right” tool in non-convex parameters spaces—. Natalia Bochkina presented a recent work, joint with Peter Green, on connecting image analysis with Bayesian asymptotics, reminding me of my early attempts at reading Ibragimov and Has’minskii in the 1990’s. Then a second work with Vladimir Spoikoini on Bayesian asymptotics with misspecified models, introducing a new notion of effective dimension. The last talk of the day was by Nils Hjort about his coming book on “Credibility, confidence and likelihood“—not yet advertised by CUP—which sounds like an attempt at resuscitating Fisher by deriving distributions in the parameter space from frequentist confidence intervals. I already discussed this notion in an earlier blog, so I am fairly skeptical about it, but the talk was representative of Nils’ highly entertaining and though-provoking style! Esp. as he sprinkled the talk with examples where MLE (and some default Bayes estimators) did not work. And reanalysed one of Chris Sims‘ example presented during his Nobel Prize talk…
Chris Sims, who got the Nobel Prize in Economics yesterday, or more precisely the Sveriges Riksbank Prize in Economic Sciences, has [also] done work in Bayesian econometrics. See for instance his talk on Why Econometrics Should Always and Everywhere Be Bayesian. Or his analysis of a counterexample of Larry Wasserman’s. He even has a tech report on adaptive Metropolis-Hastings algorithms (that apparently did not get published). He has also been teaching Bayesian statistics and econometrics at Princeton for many years, so this is a cool day for Bayesian stats! (The picture of a switching regime estimation on his webpage is actually similar to a rendering of mine from the late 90’s, when I was working on semi-Markov switching AR models with Catalin Starica, except I cannot find any trace but for the Splus code!)