Archive for ASA

Nobel prize in statistics???

Posted in Kids, Statistics, University life with tags , , , , on January 4, 2014 by xi'an

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.

MCMSki IV, Jan. 6-8, 2014, Chamonix (news #8)

Posted in Kids, Mountains, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , on September 13, 2013 by xi'an

A reposted item of news about MCMSki IV: as posted by Brad Carlin this afternoon to the Biometrics Section and Bayesian Statistical Science Section of the ASA,

The fifth joint international meeting of the IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), nicknamed “MCMSki IV”, will be held in Chamonix Mont-Blanc, France, from Monday, January 6 to Wednesday, January 8, 2014.  The meeting, the first for the newly-created BayesComp section of ISBA, will focus on all aspects of MCMC theory and methodology, including related fields like sequential Monte Carlo, approximate Bayesian computation, Hamiltonian Monte Carlo.  In contrast with the earlier meetings, it will merge the satellite Adap’ski workshop into the main meeting by having parallel invited and contributed sessions on those different themes, as well as poster sessions on both Monday and Tuesday nights.  In addition, a one-day post-conference satellite workshop on Bayesian nonparametrics, modelling and computations (“BNPski)” will be held in the same location on January 9th, 2014.

Please see our conference website,

http://www.pages.drexel.edu/~mwl25/mcmski/

for more information, including links to the preliminary program, lodging and travel information, and our conference registration page.  Please note the “early bird” registration deadline of *October 15, 2013*.

Finally (and most importantly for some), we are very pleased to announce that we have received funds from ISBA, SBSS, and other sources sufficient to help support the travel expenses of some junior investigators (defined as current PhD student, or less than 5 years since PhD). Information about how to apply for this support can be found at:

http://www.pages.drexel.edu/~mwl25/mcmski/student.html

Note that to apply you must first register with the ISBA website (if you have not done so already) and submit your abstract to the ISBA Abstract Page.  Full directions are given at the link above.  The deadline to apply for this financial support is also October 15, 2013.

We look forward to welcoming you in Chamonix this January!

Best,

Brad Carlin, Antonietta Mira, and Christian Robert
MCMSki IV conference co-organizers

10 Little’s simple ideas

Posted in Books, Statistics, University life with tags , , , , , , , , on July 17, 2013 by xi'an

“I still feel that too much of academic statistics values complex mathematics over elegant simplicity — it is necessary for a research paper to be complicated in order to be published.” Roderick Little, JASA, p.359

Roderick Little wrote his Fisher lecture, recently published in JASA, around ten simple ideas for statistics. Its title is “In praise of simplicity not mathematistry! Ten simple powerful ideas for the statistical scientist”. While this title is rather antagonistic, blaming mathematical statistics for the rise of mathematistry in the field (a term borrowed from Fisher, who also invented the adjective ‘Bayesian’), the paper focus on those 10 ideas and very little on why there is (would be) too much mathematics in statistics:

  1. Make outcomes univariate
  2. Bayes rule, for inference under an assumed model
  3. Calibrated Bayes, to keep inference honest
  4. Embrace well-designed simulation experiments
  5. Distinguish the model/estimand, the principles of estimation, and computational methods
  6. Parsimony — seek a good simple model, not the “right” model
  7. Model the Inclusion/Assignment and try to make it ignorable
  8. Consider dropping parts of the likelihood to reduce the modeling part
  9. Potential outcomes and principal stratification for causal inferenc
  10. Statistics is basically a missing data problem

“The mathematics of problems with infinite parameters is interesting, but with finite sample sizes, I would rather have a parametric model. “Mathematistry” may eschew parametric models because the asymptotic theory is too simple, but they often work well in practice.” Roderick Little, JASA, p.365

Both those rules and the illustrations that abund in the paper are reflecting upon Little’s research focus and obviously apply to his model in a fairly coherent way. However, while a mostly parametric model user myself, I fear the rejection of non-parametric techniques is far too radical. It is more and more my convinction that we cannot handle the full complexity of a realistic structure in a standard Bayesian manner and that we have to give up on the coherence and completeness goals at some point… Using non-parametrics and/or machine learning on some bits and pieces then makes sense, even though it hurts elegance and simplicity.

“However, fully Bayes inference requires detailed probability modeling, which is often a daunting task. It seems worth sacrifycing some Bayesian inferential purity if the task can be simplified.” Roderick Little, JASA, p.366

I will not discuss those ideas in detail, as some of them make complete sense to me (like Bayesian statistics laying its assumptions in the open) and others remain obscure (e.g., causality) or with limited applicability. It is overall a commendable Fisher lecture that focus on methodology and the practice of statistical science, rather than on theory. I however do not see the reason why maths should be blamed for this state of the field. Nor why mathematical statistics journals like AoS would carry some responsibility in the lack of further applicability in other fields.  Students of statistics do need a strong background in mathematics and I fear we are losing ground in this respect, at least judging by the growing difficulty in finding measure theory courses abroad for our exchange undergradutes from Paris-Dauphine. (I also find the model misspecification aspects mostly missing from this list.)

CHANCE 25(3) out

Posted in Books, Statistics, University life with tags , , , , on September 12, 2012 by xi'an

I have just received an email about CHANCE 25(3) being out, at least on-line, with my reviews of

and the main bulk of this issue being on “Culture of Statistics in Medicine”. Following a regular topic on his blog, Andrew has a column on Statistics for Cigarette Sellers, presumably linked with this entry of his. (Although I subscribe to CHANCE, I do not know how to access the on-line version and have to wait for the ASA version to appear… Another of my technical limitations, I presume.)

not only defended but also applied [to appear]

Posted in Books, Statistics, University life with tags , , , , , , , on June 12, 2012 by xi'an

Our paper with Andrew Gelman, “Not only defended but also applied”: the perceived absurdity of Bayesian inference, has been reviewed for the second time and is to appear in The American Statistician, as a discussion paper. Terrific news! This is my first discussion paper in The American Statistician (and the second in total, the first one being the re-read of JeffreysTheory of Probability.) [The updated version is now on arXiv.]

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