n-1,n,n+1, who [should] care?!

Terry Speed wrote a column in the latest IMS Bulletin (the one I received a week ago) about the choice of the denominator in the variance estimator. That is, should s² involve n (number of observations), n-1 (degrees of freedom), n+1 or anything else in its denominator? I find the question more interesting than the answer (sorry, Terry!) as it demonstrates quite forcibly that there is not a single possible choice for this estimator of the variance but that instead the “optimal” estimator is determined by the choice of the optimality criterion: this makes for a wonderful (if rather formal) playground for a class on decision theoretic statistics. And I often use it on my students. Non-Bayesian mathematical statistics courses often give the impression that there is a natural (single) estimator, when this estimator is based on an implicit choice of an optimality criterion. (This issue is illustrated in the books of Chang and of Vasishth and Broe I discussed earlier. As well as by the Stein effect, of course.) I thus deem it worthwhile to impress upon all users of statistics that there is no such single optimal choice, that unbiasedness is not a compulsory property—just as well since most parameters cannot be estimated in an unbiased manner!—, and that there is room for a subjective choice of a “best” estimator, as paradoxical as it may sound to non-statisticians.

bounded normal mean

A few days ago, one of my students, Jacopo Primavera (from La Sapienza, Roma) presented his “reading the classic” paper, namely the terrific bounded normal mean paper by my friends George Casella and Bill Strawderman (1981, Annals of Statistics). Even though I knew this paper quite well, having read (and studied) it myself many times, starting in 1987 in Purdue with Mary Ellen Bock, it was a pleasure to spend another hour on it, as I came up with new perspectives and new questions. Above are my scribbled notes on the back of the [Epson] beamer documentation. One such interesting question is whether or not it is possible to devise a computer code that would [approximately] produce the support of the least favourable prior for a given bound m (in a reasonable time). Another open question is to find the limiting bounds for which a 2 point, a 3 point, &tc., support prior is the least favourable prior. This was established in Casella and Strawderman for bounds less than 1.08 and for bounds between 1.4 and 1.6, but I am not aware of other results in that direction… Here are the slides used by Jacopo:

Publication from the frontier

In conjunction with the conference in San Antonio last March, I have received the book Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger edited by Ming-Hui Chen (University of Connecticut), Dipak K. Dey (University of Connecticut), Peter Müller (University of Texas M. D. Anderson Cancer Center), Dongchu Sun (University of Missouri- Columbia) and Keying Ye (University of Texas at San Antonio), who, incidentally, were are PhD students of Jim Berger at the time I visited Purdue University. The book has been edited in depth and so it reads very well, with contributions regrouped by chapters. Here is the table of contents:

1. Introduction.
2. Objective Bayesian inference with applications.
3. Bayesian decision based estimation and predictive inference.
4. Bayesian model selection and hypothesis tests.
5. Bayesian computer models.
6. Bayesian nonparametrics and semi-parametrics.
7. Bayesian case influence and frequentist interface.
8. Bayesian clinical trials.
9. Bayesian methods for genomics, molecular, and systems biology.
10. Bayesian data mining and machine learning.
11. Bayesian inference in political and social sciences, finance, and marketing.
12. Bayesian categorical data analysis.
13. Bayesian geophysical, spatial, and temporal statistics.
14. Posterior simulation and Monte Carlo methods.

whose final chapter (the only one missing Bayesian from the title!) contains our contribution with Jean-Michel Marin.

Short Monte Carlo course in San Antonio

Just a reminder that I will teach a very short course on March 17 in San Antonio, Texas, based on our “Introducing Monte Carlo Methods with R” book, in the first day of the meeting  Frontiers of Statistical Decision Making and Bayesian Analysis, in honour of Jim Berger, There still are a few places left on the four courses provided that day, register now!