**A**fter a fairly long delay (since the first version was posted and submitted in December 2014), we eventually revised and resubmitted our paper with Kaniav Kamary [who has now graduated], Kerrie Mengersen, and Judith Rousseau on the final day of 2018. The main reason for this massive delay is mine’s, as I got fairly depressed by the general tone of the dozen of reviews we received after submitting the paper as a Read Paper in the Journal of the Royal Statistical Society. Despite a rather opposite reaction from the community (an admittedly biased sample!) including two dozens of citations in other papers. (There seems to be a pattern in my submissions of Read Papers, witness our earlier and unsuccessful attempt with Christophe Andrieu in the early 2000’s with the paper on controlled MCMC, leading to 121 citations so far according to G scholar.) Anyway, thanks to my co-authors keeping up the fight!, we started working on a revision including stronger convergence results, managing to show that the approach leads to an optimal separation rate, contrary to the Bayes factor which has an extra √log(n) factor. This may sound paradoxical since, while the Bayes factor converges to 0 under the alternative model exponentially quickly, the convergence rate of the mixture weight α to 1 is of order 1/√n, but this does not mean that the separation rate of the procedure based on the mixture model is worse than that of the Bayes factor. On the contrary, while it is well known that the Bayes factor leads to a separation rate of order √log(n) in parametric models, we show that our approach can lead to a testing procedure with a better separation rate of order 1/√n. We also studied a non-parametric setting where the null is a specified family of distributions (e.g., Gaussians) and the alternative is a Dirichlet process mixture. Establishing that the posterior distribution concentrates around the null at the rate √log(n)/√n. We thus resubmitted the paper for publication, although not as a Read Paper, with hopefully more luck this time!

## Archive for testing as mixture estimation

## mixture modelling for testing hypotheses

Posted in Books, Statistics, University life with tags Bayes factor, Bayesian hypothesis testing, Christophe Andrieu, controlled MCMC, JRSSB, peer review, Read paper, revision, testing as mixture estimation, Ultimixt, University of Bristol on January 4, 2019 by xi'an## R typos

Posted in Books, Kids, R, Statistics, Travel, University life with tags Amsterdam, Bayesian Analysis, MCMskv, Metropolis-Hastings algorithm, mixtures, Monte Carlo Statistical Methods, R, random walk, testing as mixture estimation on January 27, 2016 by xi'an**A**t MCMskv, Alexander Ly (from Amsterdam) pointed out to me some R programming mistakes I made in the introduction to Metropolis-Hastings algorithms I wrote a few months ago for the Wiley on-line encyclopedia! While the outcome (Monte Carlo posterior) of the corrected version is moderately changed this is nonetheless embarrassing! The example (if not the R code) was a mixture of a Poisson and a Geometric distributions borrowed from our testing as mixture paper. Among other things, I used a flat prior on the mixture weights instead of a Beta(1/2,1/2) prior *and* a simple log-normal random walk on the mean parameter instead of a more elaborate second order expansion discussed in the text. And I also inverted the probabilities of success and failure for the Geometric density. The new version is now available on arXiv, and hopefully soon on the Wiley site, but one (the?) fact worth mentioning here is that the (right) corrections in the R code first led to overflows, because I was using the Beta random walk Be(εp,ε(1-p)) which major drawback I discussed here a few months ago. With the drag that nearly zero or one values of the weight parameter produced infinite values of the density… Adding 1 (or 1/2) to each parameter of the Beta proposal solved the problem. And led to a posterior on the weight still concentrating on the correct corner of the unit interval. In any case, a big thank you to Alexander for testing the R code and spotting out the several mistakes…

## a vignette on Metropolis

Posted in Books, Kids, R, Statistics, Travel, University life with tags Columbia University, Introducing Monte Carlo Methods with R, Metropolis-Hastings algorithm, mixture, New York city, testing as mixture estimation, vignette on April 13, 2015 by xi'an**O**ver the past week, I wrote a short introduction to the Metropolis-Hastings algorithm, mostly in the style of our Introduction to Monte Carlo with R book, that is, with very little theory and worked-out illustrations on simple examples. (And partly over the Atlantic on my flight to New York and Columbia.) This vignette is intended for the Wiley StatsRef: Statistics Reference Online Series, modulo possible revision. Again, nothing novel therein, except for new examples.