I received this email from Wiley with the great figure that JRSS Series B has now reached a 5.721 impact factor. Which makes it the first journal in Statistics from this perspective. Congrats to editors Gareth Roberts, Piotr Fryzlewicz and Ingrid Van Keilegom for this achievement! An amazing jump from the 2009 figure of 2.84…!
Archive for Series B
The September issue of [JRSS] Series B I received a few days ago is of particular interest to me. (And not as an ex-co-editor since I was never involved in any of those papers!) To wit: a paper by Hani Doss and Aixin Tan on evaluating normalising constants based on MCMC output, a preliminary version I had seen at a previous JSM meeting, a paper by Nick Polson, James Scott and Jesse Windle on the Bayesian bridge, connected with Nick’s talk in Boston earlier this month, yet another paper by Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar and Michael Jordan on the bag of little bootstraps, which presentation I heard Michael deliver a few times when he was in Paris. (Obviously, this does not imply any negative judgement on the other papers of this issue!)
For instance, Doss and Tan consider the multiple mixture estimator [my wording, the authors do not give the method a name, referring to Vardi (1985) but missing the connection with Owen and Zhou (2000)] of k ratios of normalising constants, namely
where the z’s are the normalising constants and with possible different numbers of iterations of each Markov chain. An interesting starting point (that Hans Künsch had mentioned to me a while ago but that I had since then forgotten) is that the problem was reformulated by Charlie Geyer (1994) as a quasi-likelihood estimation where the ratios of all z’s relative to one reference density are the unknowns. This is doubling interesting, actually, because it restates the constant estimation problem into a statistical light and thus somewhat relates to the infamous “paradox” raised by Larry Wasserman a while ago. The novelty in the paper is (a) to derive an optimal estimator of the ratios of normalising constants in the Markov case, essentially accounting for possibly different lengths of the Markov chains, and (b) to estimate the variance matrix of the ratio estimate by regeneration arguments. A favourite tool of mine, at least theoretically as practically useful minorising conditions are hard to come by, if at all available.
Following the exciting and innovative talks, posters and discussions at MCMski IV, the editor of Statistics and Computing, Mark Girolami (who also happens to be the new president-elect of the BayesComp section of ISBA, which is taking over the management of future MCMski meetings), kindly proposed to publish a special issue of the journal open to all participants to the meeting. Not only to speakers, mind, but to all participants.
So if you are interested in submitting a paper to this special issue of a computational statistics journal that is very close to our MCMski themes, I encourage you to do so. (Especially if you missed the COLT 2014 deadline!) The deadline for submissions is set on March 15 (a wee bit tight but we would dearly like to publish the issue in 2014, namely the same year as the meeting.) Submissions are to be made through the Statistics and Computing portal, with a mention that they are intended for the special issue.
An editorial committee chaired by Antonietta Mira and composed of Christophe Andrieu, Brad Carlin, Nicolas Chopin, Jukka Corander, Colin Fox, Nial Friel, Chris Holmes, Gareth Jones, Peter Müller, Antonietta Mira, Geoff Nicholls, Gareth Roberts, Håvård Rue, Robin Ryder, and myself, will examine the submissions and get back within a few weeks to the authors. In a spirit similar to the JRSS Read Paper procedure, submissions will first be examined collectively, before being sent to referees. We plan to publish the reviews as well, in order to include a global set of comments on the accepted papers. We intend to do it in The Economist style, i.e. as a set of edited anonymous comments. Usual instructions for Statistics and Computing apply, with the additional requirements that the paper should be around 10 pages and include at least one author who took part in MCMski IV.
The Journal of the Royal Statistical Society, Series B, has a new cover, a new colour and a new co-editor. As can be seen from the above shots, the colour is now a greenish ochre, with a picture of pedestrians on a brick plaza as a background, not much related to statistical methodology as far as I can tell. More importantly, the new co-editor for the coming four years is Piotr Fryzlewicz, professor at the London School of Economics, who will share the burden with Ingrid van Keilegom professor from UCL (Louvain-la-Neuve) who is now starting her third year… My friend, colleague and successor as Series B editor Gareth Roberts is now retiring after four years of hard work towards making Series B one of the top journals in Statistics. Thanks Gareth and best wishes to Ingrid and Piotr!
Here is a quote from Mervyn Stone’s discussion of the DIC paper in Series B
“The paper is rather economical with the ‘truth’. The truth of pt(Y) corresponds fixedly to the conditions of the experimental or observational set-up that ensures independent future replication Yrep or internal independence of y = (y1,…,yn) (not excluding an implicit concomitant x). For pt(Y) ≈ p(Y|θt), θt must parameterize a scientifically plausible family of alternative distributions of Y under those conditions and is therefore a necessary ‘focus’ if the ‘good [true] model’ idea is to be invoked: think of tossing a bent coin. Changing focus is not an option.”
that I found most amusing (and relevant)! Elías Moreno and I wrote our discussions from Newcastle-upon-Tyne for Series B (and arXived them as well, with a wee bit of confusion when I listed the affiliations: I am not [yet] associated with la Universidad de Las Palmas de Gran Canaria..!).
After a very, very long delay, we eventually re-revised our paper about necessary and sufficient conditions on summary statistics to be relevant for model choice (i.e. to lead to consistent tests). Reasons, both good and bad, abound for this delay! Some (rather bad) were driven by the completion of a certain new edition… Some (fairly good) are connected with the requests from the Series B editorial team, towards improving our methodological input. As a result we put more emphasis on the post-ABC cross-checking for the relevance of the summary choice, via a predictive posterior evaluation of the means of the summary statistic under both models and a test for mean equality. And re-ran a series of experiments on a three population population genetic example. Plus, on the side, simplified some of our assumptions. I dearly hope the paper can make it through but am also looking forward the opinion of the Series B editorial team The next version of Relevant statistics for Bayesian model choice should be arXived by now (meaning when this post appears!).
A few days ago, Dennis Prangle, Paul Fernhead, and their co-authors from New Zealand have posted on arXiv their (long-awaited) study of the selection of summary statistics for ABC model choice. And I read it during my trip to England, in trains and planes, if not when strolling in the beautiful English countryside as above.
As posted several times on this ‘Og, the crux of the analysis is that the Bayes factor is a good type of summary when comparing two models, this result extending to more model by considering instead the vector of evidences. As in the initial Read Paper by Fearnhead and Prangle, there is no true optimality in using the Bayes factor or vector of evidences, strictly speaking, besides the fact that the vector of evidences is minimal sufficient for the marginal models (integrating out the parameters). (This was a point made in my discussion.) The implementation of the principle is similar to this Read Paper setting as well: run a pilot ABC simulation, estimate the vector of evidences, and re-run the main ABC simulation using this estimate as the summary statistic. The paper contains a simulation study using some of our examples (in Marin et al., 2012), as well as an application to genetic bacterial data. Continue reading