Archive for cross validated

STEM forums

Posted in Books, R, Statistics, University life with tags , , , , , on August 15, 2014 by xi'an

“I can calculate the movement of stars, but not the madness of men.” Isaac Newton

When visiting the exhibition hall at JSM 2014, I spoke with people from STEM forums on the Springer booth. The concept of STEM (why STEM? Nothing to do with STAN! Nor directly with Biology. It stands as the accronym for Science, Technology, Engineering, and Mathematics.) is to create a sort of peer-reviewed Cross Validated where questions would be filtered (in order to avoid the most basic questions like “How can I learn about Bayesian statistics without opening a book?” or “What is the Binomial distribution?” that often clutter the Stack Exchange boards). That’s an interesting approach which I will monitor in the future, as on the one hand, it would be nice to have a Statistics forum without “lazy undergraduate” questions as one of my interlocutors put, and on the other hand, to see how STEM forums can compete with the well-established Cross Validated and its core of dedicated moderators and editors. I left the booth with a neat tee-shirt exhibiting the above quote as well as alpha-tester on the back: STEM forums is indeed calling for entries into the Statistics section, with rewards of ebooks for the first 250 entries and a sweepstakes offering a free trip to Seattle next year!

numbers

Posted in Statistics with tags , , , , on December 2, 2012 by xi'an

Last week, the ‘Og reached 2000 posts, 4000 comments, and 600,000 views. These are the most popular entries

In{s}a(ne)!! 8,277
“simply start over and build something better” 7,069
George Casella 5,757
Julien on R shortcomings 3,226
Sudoku via simulated annealing 2,995
#2 blog for the statistics geek?! 2,676
Bayesian p-values 2,395
Solution manual to Bayesian Core on-line 2,111
Of black swans and bleak prospects 2,009
Solution manual for Introducing Monte Carlo Methods with R 1,996
Parallel processing of independent Metropolis-Hastings algorithms 1,862
Bayes’ Theorem 1,721
Bayes on the Beach 2010 [2] 1,718
Do we need an integrated Bayesian/likelihood inference? 1,585
Coincidence in lotteries 1,486
Julian Besag 1945-2010 1,407

As noted earlier this year, the posts on the future of R remain the top visited posts. Sadly and comfortingly, the entry I wrote for mourning George passing away was the most visited this year. Bayes on the Beach 2010 [2] gets traffic for the wrong reason, simply for mentioning Surfers’ Paradise… As a coincidence, I also reached the 4000 level on Stack Exchange – Cross Validation, but this is so completely anecdotal…

estimating a constant (not really)

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

Larry Wasserman wrote a blog entry on the normalizing constant paradox, where he repeats that he does not understand my earlier point…Let me try to recap here this point and the various comments I made on StackExchange (while keeping in mind all this is for intellectual fun!)

The entry is somehow paradoxical in that Larry acknowledges (in that post) that the analysis in his book, All of Statistics, is wrong. The fact that “g(x)/c is a valid density only for one value of c” (and hence cannot lead to a notion of likelihood on c) is the very reason why I stated that there can be no statistical inference nor prior distribution about c: a sample from f does not bring statistical information about c and there can be no statistical estimate of c based on this sample. (In case you did not notice, I insist upon statistical!)

To me this problem is completely different from a statistical problem, at least in the modern sense: if I need to approximate the constant c—as I do in fact when computing Bayes factors—, I can produce an arbitrarily long sample from a certain importance distribution and derive a converging (and sometimes unbiased) approximation of c. Once again, this is Monte Carlo integration, a numerical technique based on the Law of Large Numbers and the stabilisation of frequencies. (Call it a frequentist method if you wish. I completely agree that MCMC methods are inherently frequentist in that sense, And see no problem with this because they are not statistical methods. Of course, this may be the core of the disagreement with Larry and others, that they call statistics the Law of Large Numbers, and I do not. This lack of separation between both notions also shows up in a recent general public talk on Poincaré’s mistakes by Cédric Villani! All this may just mean I am irremediably Bayesian, seeing anything motivated by frequencies as non-statistical!) But that process does not mean that c can take a range of values that would index a family of densities compatible with a given sample. In this Monte Carlo integration approach, the distribution of the sample is completely under control (modulo the errors induced by pseudo-random generation). This approach is therefore outside the realm of Bayesian analysis “that puts distributions on fixed but unknown constants”, because those unknown constants parameterise the distribution of an observed sample. Ergo, c is not a parameter of the sample and the sample Larry argues about (“we have data sampled from a distribution”) contains no information whatsoever about c that is not already in the function g. (It is not “data” in this respect, but a stochastic sequence that can be used for approximation purposes.) Which gets me back to my first argument, namely that c is known (and at the same time difficult or impossible to compute)!

Let me also answer here the comments on “why is this any different from estimating the speed of light c?” “why can’t you do this with the 100th digit of π?” on the earlier post or on StackExchange. Estimating the speed of light means for me (who repeatedly flunked Physics exams after leaving high school!) that we have a physical experiment that measures the speed of light (as the original one by Rœmer at the Observatoire de Paris I visited earlier last week) and that the statistical analysis infers about c by using those measurements and the impact of the imprecision of the measuring instruments (as we do when analysing astronomical data). If, now, there exists a physical formula of the kind

c=\int_\Xi \psi(\xi) \varphi(\xi) \text{d}\xi

where φ is a probability density, I can imagine stochastic approximations of c based on this formula, but I do not consider it a statistical problem any longer. The case is thus clearer for the 100th digit of π: it is also a fixed number, that I can approximate by a stochastic experiment but on which I cannot attach a statistical tag. (It is 9, by the way.) Throwing darts at random as I did during my Oz tour is not a statistical procedure, but simple Monte Carlo à la Buffon…

Overall, I still do not see this as a paradox for our field (and certainly not as a critique of Bayesian analysis), because there is no reason a statistical technique should be able to address any and every numerical problem. (Once again, Persi Diaconis would almost certainly differ, as he defended a Bayesian perspective on numerical analysis in the early days of MCMC…) There may be a “Bayesian” solution to this particular problem (and that would nice) and there may be none (and that would be OK too!), but I am not even convinced I would call this solution “Bayesian”! (Again, let us remember this is mostly for intellectual fun!)

estimating a constant

Posted in Books, Statistics with tags , , , , , , , , , on October 3, 2012 by xi'an

Paulo (a.k.a., Zen) posted a comment in StackExchange on Larry Wasserman‘s paradox about Bayesians and likelihoodists (or likelihood-wallahs, to quote Basu!) being unable to solve the problem of estimating the normalising constant c of the sample density, f, known up to a constant

f(x) = c g(x)

(Example 11.10, page 188, of All of Statistics)

My own comment is that, with all due respect to Larry!, I do not see much appeal in this example, esp. as a potential criticism of Bayesians and likelihood-wallahs…. The constant c is known, being equal to

1/\int_\mathcal{X} g(x)\text{d}x

If c is the only “unknown” in the picture, given a sample x1,…,xn, then there is no statistical issue whatsoever about the “problem” and I do not agree with the postulate that there exist estimators of c. Nor priors on c (other than the Dirac mass on the above value). This is not in the least a statistical problem but rather a numerical issue.That the sample x1,…,xn can be (re)used through a (frequentist) density estimate to provide a numerical approximation of c

\hat c = \hat f(x_0) \big/ g(x_0)

is a mere curiosity. Not a criticism of alternative statistical approaches: e.g., I could also use a Bayesian density estimate…

Furthermore, the estimate provided by the sample x1,…,xn is not of particular interest since its precision is imposed by the sample size n (and converging at non-parametric rates, which is not a particularly relevant issue!), while I could use importance sampling (or even numerical integration) if I was truly interested in c. I however find the discussion interesting for many reasons

  1. it somehow relates to the infamous harmonic mean estimator issue, often discussed on the’Og!;
  2. it brings more light on the paradoxical differences between statistics and Monte Carlo methods, in that statistics is usually constrained by the sample while Monte Carlo methods have more freedom in generating samples (up to some budget limits). It does not make sense to speak of estimators in Monte Carlo methods because there is no parameter in the picture, only “unknown” constants. Both fields rely on samples and probability theory, and share many features, but there is nothing like a “best unbiased estimator” in Monte Carlo integration, see the case of the “optimal importance function” leading to a zero variance;
  3. in connection with the previous point, the fascinating Bernoulli factory problem is not a statistical problem because it requires an infinite sequence of Bernoullis to operate;
  4. the discussion induced Chris Sims to contribute to StackExchange!

another X’idated question

Posted in Books, Kids, R, Statistics, University life with tags , , , , on February 23, 2012 by xi'an

An X’idated reader of Monte Carlo Statistical Methods had trouble with our Example 3.13, the very one our academic book reviewer disliked so much as to “diverse [sic] a 2 star”. The issue is with computing the integral

\mathfrak{I}=\int\limits_{-\infty}^{\infty}{\sqrt{\left|\frac{\theta}{1-\theta}\right|}}f(\theta)\text{d}\theta

when f is the Student’s t(5) distribution density. In our book, we compare a few importance sampling solutions, but it seems someone (the instructor?) suggested to this X’idated reader to use a mixture importance density

0.5\{{{g}_{1}}(\theta )+{{g}_{2}}(\theta )\},

with

{{g}_{1}}(\theta )=\dfrac{1}{\pi }\dfrac{1}{1+{{\theta}^{2}}}

and

{{g}_{2}}(\theta )=\dfrac{1}{4\sqrt{\left|1-\theta\right|}}\quad\text{on}\quad[0,2]

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