Archive for the Uncategorized Category

L’Affiche Rouge (Feb. 21, 1944)

Posted in Uncategorized with tags , , , , , on February 21, 2014 by xi'an

my week at War[wick]

Posted in pictures, Running, Statistics, Travel, Uncategorized with tags , , , , , , , , , on February 1, 2014 by xi'an

This was a most busy and profitable week in Warwick as, in addition to meeting with local researchers and students on a wide range of questions and projects, giving an extended seminar to MASDOC students, attending as many seminars as humanly possible (!), and preparing a 5k race by running in the Warwickshire countryside (in the dark and in the rain), I received the visits of Kerrie Mengersen, Judith Rousseau and Jean-Michel Marin, with whom I made some progress on papers we are writing together. In particular, Jean-Michel and I wrote the skeleton of a paper we (still) plan to submit to COLT 2014 next week. And Judith, Kerrie and I drafted new if paradoxical aconnections between empirical likelihood and model selection. Jean-Michel and Judith also gave talks at the CRiSM seminar, Jean-Michel presenting the latest developments on the convergence of our AMIS algorithm, Judith summarising several papers on the analysis of empirical Bayes methods in non-parametric settings.

2013 in review [by WordPress]

Posted in Uncategorized with tags , on December 31, 2013 by xi'an

The WordPress.com stats helper monkeys prepared a 2013 annual report for this blog.

Here’s an excerpt:

The Louvre Museum has 8.5 million visitors per year. This blog was viewed about 250,000 times in 2013. If it were an exhibit at the Louvre Museum, it would take about 11 days for that many people to see it.

Click here to see the complete report.

beta HPD

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

While writing an introductory chapter on Bayesian analysis (in French), I came by the issue of computing an HPD region when the posterior distribution is a Beta B(α,β) distribution… There is no analytic solution and hence I resorted to numerical resolution (provided here for α=117.5, β=115.5):

f=function(p){

  # find the symmetric
  g=function(x){return(x-p*((1-p)/(1-x))^(115.5/117.5))}
  return(uniroot(g,c(.504,.99))$root)}

ff=function(alpha){

  # find the coverage
  g=function(x){return(x-p*((1-p)/(1-x))^(115.5/117.5))}
  return(uniroot(g,c(.011,.49))$root)}

and got the following return:

> ff(.95)
[1] 0.4504879
> f(ff(.95))
[1] 0.5580267

which was enough for my simple book illustration… Since (.450,558) is then the HPD region at credible level 0.95.

Deborah Mayo’s talk in Montréal (JSM 2013)

Posted in Books, Statistics, Uncategorized with tags , , , , , , on July 31, 2013 by xi'an

As posted on her blog, Deborah Mayo is giving a lecture at JSM 2013 in Montréal about why Birnbaum’s derivation of the Strong Likelihood Principle (SLP) is wrong. Or, more accurately, why “WCP entails SLP”. It would have been a great opportunity to hear Deborah presenting her case and I am sorry I am missing this opportunity. (Although not sorry to be in the beautiful Dolomites at that time.) Here are the slides:

Deborah’s argument is the same as previously: there is no reason for the inference in the mixed (or Birnbaumized) experiment to be equal to the inference in the conditional experiment. As previously, I do not get it: the weak conditionality principle (WCP) implies that inference from the mixture output, once we know which component is used (hence rejecting the “and we don’t know which” on slide 8), should only be dependent on that component. I also fail to understand why either WCP or the Birnbaum experiment refers to a mixture (sl.13) in that the index of the experiment is assumed to be known, contrary to mixtures. Thus (still referring at slide 13), the presentation of Birnbaum’s experiment is erroneous. It is indeed impossible to force the outcome of y* if tail and of x* if head but it is possible to choose the experiment index at random, 1 versus 2, and then, if y* is observed, to report (E1,x*) as a sufficient statistic. (Incidentally, there is a typo on slide 15, it should be “likewise for x*”.)

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