down-under ABC paper accepted in JCGS!

Great news!, the ABC paper we had originally started in 2012 in Melbourne with Gael Martin and Brendan MacCabe, before joining forces with David Frazier and Worapree Maneesoothorn, in expanding its scope to using auxiliary likelihoods to run ABC in state-space models, just got accepted in the Journal of Computational and Graphical Statistics. A reason to celebrate with a Mornington Peninsula Pinot Gris wine next time I visit Monash!

weakly informative reparameterisations

Our paper, weakly informative reparameterisations of location-scale mixtures, with Kaniav Kamary and Kate Lee, got accepted by JCGS! Great news, which comes in perfect timing for Kaniav as she is currently applying for positions. The paper proposes a unidimensional mixture Bayesian modelling based on the first and second moment constraints, since these turn the remainder of the parameter space into a compact. While we had already developed an associated R package, Ultimixt, the current editorial policy of JCGS imposes the R code used to produce all results to be attached to the submission and it took us a few more weeks than it should have to produce a directly executable code, due to internal library incompatibilities. (For this entry, I was looking for a link to our special JCGS issue with my picture of Edinburgh but realised I did not have this picture.)

a lifetime word limit…

“Exceptions might have to be made for experts such as statisticians and bioinformaticians whose skills are required on many papers.”

One of these weird editorials periodically occurring in Nature. By Brian Martinson, suggesting that the number of words allotted to a scientist should be capped. Weird, indeed, and incomprehensible that Nature wastes one of its so desperately sought journal page on such a fantastic (in the sense of fantasy, not as in great!) proposal. With sentences like “if we don’t address our own cognitive biases and penchant for compelling narratives, word limits could exacerbate tendencies to publish only positive findings, leading researchers to explore blind alleys that others’ negative results could have illuminated” not making much sense even in this fantasy academic world… As for the real world, the list of impossibilities and contradictions stemming from this proposal would certainly eat all of my allotted words. Even those allotted to a statistician. The supreme irony of the (presumably tongue-in-cheek) editorial is that the author himself does not seem particularly concerned by capping his own number of papers! (Nice cover, by the way!)

what to do with refereed conference proceedings?

In the recent days, we have had a lively discussion among AEs of the Annals of Statistics, as to whether or not set up a policy regarding publications of documents that have already been published in a shortened (8 pages) version in a machine learning conference like NIPS. Or AISTATS. While I obviously cannot disclose details here, the debate is quite interesting and may bring the machine learning and statistics communities closer if resolved in a certain way. My own and personal opinion on that matter is that what matters most is what’s best for Annals of Statistics rather than the authors’ tenure or the different standards in the machine learning community. If the submitted paper is based on a brilliant and novel idea that can appeal to a sufficiently wide part of the readership and if the maths support of that idea is strong enough, we should publish the paper. Whether or not an eight-page preliminary version has been previously published in a conference proceeding like NIPS does not seem particularly relevant to me, as I find those short papers mostly unreadable and hence do not read them. Since Annals of Statistics runs an anti-plagiarism software that is most likely efficient, blatant cases of duplications could be avoided. Of course, this does not solve all issues and papers with similar contents can and will end up being published. However, this is also the case for statistics journals and statistics, in the sense that brilliant ideas sometimes end up being split between two or three major journals.

Incredibly ugly squalid pictures…

Well, this is not a common teaser to attract readers, but a comment on one of my graphs in the second revision of our paper Adaptivity for approximate Bayesian computation algorithms: a population Monte Carlo approach, written with Marc Beaumont, Jean-Marie Cornuet, and Jean-Michel Marin, and (re-re-)submitted to Biometrika… Not something I’d like to hear about my graphs, thank you!, as the pdf version of the graph on the right actually looks better than that one…. Anyway, we revised the paper towards less squalidness, replacing histogram with density using the “h” type in R. The major request on the revision was to get under eight pages in order to fit inside the Miscelanea section of Biometrika. Changes are thus mostly cosmetic compared with the earlier version, as you can check on the arXiv list of versions. The background for the paper and the earlier paper of Sisson, Fan, and Tanaka (2007, PNAS) it analyses, are described in this earlier post.