Archive for University of Warwick

statistical modelling of citation exchange between statistics journals

Posted in Books, Statistics, University life with tags , , , , , on April 10, 2015 by xi'an

Cristiano Varin, Manuela Cattelan and David Firth (Warwick) have written a paper on the statistical analysis of citations and index factors, paper that is going to be Read at the Royal Statistical Society next May the 13th. And hence is completely open to contributed discussions. Now, I have written several entries on the ‘Og about the limited trust I set to citation indicators, as well as about the abuse made of those. However I do not think I will contribute to the discussion as my reservations are about the whole bibliometrics excesses and not about the methodology used in the paper.

The paper builds several models on the citation data provided by the “Web of Science” compiled by Thompson Reuters. The focus is on 47 Statistics journals, with a citation horizon of ten years, which is much more reasonable than the two years in the regular impact factor. A first feature of interest in the descriptive analysis of the data is that all journals have a majority of citations from and to journals outside statistics or at least outside the list. Which I find quite surprising. The authors also build a cluster based on the exchange of citations, resulting in rather predictable clusters, even though JCGS and Statistics and Computing escape the computational cluster to end up in theory and methods along Annals of Statistics and JRSS Series B.

In addition to the unsavoury impact factor, a ranking method discussed in the paper is the eigenfactor score that starts with a Markov exploration of articles by going at random to one of the papers in the reference list and so on. (Which shares drawbacks with the impact factor, e.g., in that it does not account for the good or bad reason the paper is cited.) Most methods produce the Big Four at the top, with Series B ranked #1, and Communications in Statistics A and B at the bottom, along with Journal of Applied Statistics. Again, rather anticlimactic.

The major modelling input is based on Stephen Stigler’s model, a generalised linear model on the log-odds of cross citations. The Big Four once again receive high scores, with Series B still much ahead. (The authors later question the bias due to the Read Paper effect, but cannot easily evaluate this impact. While some Read Papers like Spiegelhalter et al. 2002 DIC do generate enormous citation traffic, to the point of getting re-read!, other journals also contain discussion papers. And are free to include an on-line contributed discussion section if they wish.) Using an extra ranking lasso step does not change things.

In order to check the relevance of such rankings, the authors also look at the connection with the conclusions of the (UK) 2008 Research Assessment Exercise. They conclude that the normalised eigenfactor score and Stigler model are more correlated with the RAE ranking than the other indicators.  Which means either that the scores are good predictors or that the RAE panel relied too heavily on bibliometrics! The more global conclusion is that clusters of journals or researchers have very close indicators, hence that ranking should be conducted with more caution that it is currently. And, more importantly, that reverting the indices from journals to researchers has no validation and little information.

at The X

Posted in pictures, Running, Travel, Wines with tags , , , , , , , on March 15, 2015 by xi'an

TheX

snapshot from Gibbet Hill

Posted in pictures, Travel, University life with tags , , , , on March 5, 2015 by xi'an

Gibbet

accelerating Metropolis-Hastings algorithms by delayed acceptance

Posted in Books, Statistics, University life with tags , , , , , , , , on March 5, 2015 by xi'an

Marco Banterle, Clara Grazian, Anthony Lee, and myself just arXived our paper “Accelerating Metropolis-Hastings algorithms by delayed acceptance“, which is an major revision and upgrade of our “Delayed acceptance with prefetching” paper of last June. Paper that we submitted at the last minute to NIPS, but which did not get accepted. The difference with this earlier version is the inclusion of convergence results, in particular that, while the original Metropolis-Hastings algorithm dominates the delayed version in Peskun ordering, the later can improve upon the original for an appropriate choice of the early stage acceptance step. We thus included a new section on optimising the design of the delayed step, by picking the optimal scaling à la Roberts, Gelman and Gilks (1997) in the first step and by proposing a ranking of the factors in the Metropolis-Hastings acceptance ratio that speeds up the algorithm.  The algorithm thus got adaptive. Compared with the earlier version, we have not pursued the second thread of prefetching as much, simply mentioning that prefetching and delayed acceptance could be merged. We have also included a section on the alternative suggested by Philip Nutzman on the ‘Og of using a growing ratio rather than individual terms, the advantage being the probability of acceptance stabilising when the number of terms grows, with the drawback being that expensive terms are not always computed last. In addition to our logistic and mixture examples, we also study in this version the MALA algorithm, since we can postpone computing the ratio of the proposals till the second step. The gain observed in one experiment is of the order of a ten-fold higher efficiency. By comparison, and in answer to one comment on Andrew’s blog, we did not cover the HMC algorithm, since the preliminary acceptance step would require the construction of a proxy to the acceptance ratio, in order to avoid computing a costly number of derivatives in the discretised Hamiltonian integration.

the fundamental incompatibility of HMC and data subsampling

Posted in Books, Statistics, University life with tags , , , , , , on February 23, 2015 by xi'an

the pond in front of the Zeeman building, University of Warwick, July 01, 2014Last week, Michael Betancourt, from WarwickarXived a neat wee note on the fundamental difficulties in running HMC on a subsample of the original data. The core message is that using only one fraction of the data to run an HMC with the hope that it will preserve the stationary distribution does not work. The only way to recover from the bias is to use a Metropolis-Hastings step using the whole data, a step that both kills most of the computing gain and has very low acceptance probabilities. Even the strategy that subsamples for each step in a single trajectory fails: there cannot be a significant gain in time without a significant bias in the outcome. Too bad..! Now, there are ways of accelerating HMC, for instance by parallelising the computation of gradients but, just as in any other approach (?), the information provided by the whole data is only available when looking at the whole data.

Alan Turing Institute

Posted in Books, pictures, Running, Statistics, University life with tags , , , , , , , , , on February 10, 2015 by xi'an

 

The University of Warwick is one of the five UK Universities (Cambridge, Edinburgh, Oxford, Warwick and UCL) to be part of the new Alan Turing Institute.To quote from the University press release,  “The Institute will build on the UK’s existing academic strengths and help position the country as a world leader in the analysis and application of big data and algorithm research. Its headquarters will be based at the British Library at the centre of London’s Knowledge Quarter.” The Institute will gather researchers from mathematics, statistics, computer sciences, and connected fields towards collegial and focussed research , which means in particular that it will hire a fairly large number of researchers in stats and machine-learning in the coming months. The Department of Statistics at Warwick was strongly involved in answering the call for the Institute and my friend and colleague Mark Girolami will the University leading figure at the Institute, alas meaning that we will meet even less frequently! Note that the call for the Chair of the Alan Turing Institute is now open, with deadline on March 15. [As a personal aside, I find the recognition that Alan Turing’s genius played a pivotal role in cracking the codes that helped us win the Second World War. It is therefore only right that our country’s top universities are chosen to lead this new institute named in his honour. by the Business Secretary does not absolve the legal system that drove Turing to suicide….]

Bayesian computation: fore and aft

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , on February 6, 2015 by xi'an

BagneuxWith my friends Peter Green (Bristol), Krzysztof Łatuszyński (Warwick) and Marcello Pereyra (Bristol), we just arXived the first version of “Bayesian computation: a perspective on the current state, and sampling backwards and forwards”, which first title was the title of this post. This is a survey of our own perspective on Bayesian computation, from what occurred in the last 25 years [a  lot!] to what could occur in the near future [a lot as well!]. Submitted to Statistics and Computing towards the special 25th anniversary issue, as announced in an earlier post.. Pulling strength and breadth from each other’s opinion, we have certainly attained more than the sum of our initial respective contributions, but we are welcoming comments about bits and pieces of importance that we miss and even more about promising new directions that are not posted in this survey. (A warning that is should go with most of my surveys is that my input in this paper will not differ by a large margin from ideas expressed here or in previous surveys.)

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