Archive for Royal Statistical Society

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.

Quasi-Monte Carlo sampling

Posted in Books, Kids, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , on December 10, 2014 by xi'an

RSS wine“The QMC algorithm forces us to write any simulation as an explicit function of uniform samples.” (p.8)

As posted a few days ago, Mathieu Gerber and Nicolas Chopin will read this afternoon a Paper to the Royal Statistical Society on their sequential quasi-Monte Carlo sampling paper.  Here are some comments on the paper that are preliminaries to my written discussion (to be sent before the slightly awkward deadline of Jan 2, 2015).

Quasi-Monte Carlo methods are definitely not popular within the (mainstream) statistical community, despite regular attempts by respected researchers like Art Owen and Pierre L’Écuyer to induce more use of those methods. It is thus to be hoped that the current attempt will be more successful, it being Read to the Royal Statistical Society being a major step towards a wide diffusion. I am looking forward to the collection of discussions that will result from the incoming afternoon (and bemoan once again having to miss it!).

“It is also the resampling step that makes the introduction of QMC into SMC sampling non-trivial.” (p.3)

At a mathematical level, the fact that randomised low discrepancy sequences produce both unbiased estimators and error rates of order

\mathfrak{O}(N^{-1}\log(N)^{d-}) \text{ at cost } \mathfrak{O}(N\log(N))

means that randomised quasi-Monte Carlo methods should always be used, instead of regular Monte Carlo methods! So why is it not always used?! The difficulty stands [I think] in expressing the Monte Carlo estimators in terms of a deterministic function of a fixed number of uniforms (and possibly of past simulated values). At least this is why I never attempted at crossing the Rubicon into the quasi-Monte Carlo realm… And maybe also why the step had to appear in connection with particle filters, which can be seen as dynamic importance sampling methods and hence enjoy a local iid-ness that relates better to quasi-Monte Carlo integrators than single-chain MCMC algorithms.  For instance, each resampling step in a particle filter consists in a repeated multinomial generation, hence should have been turned into quasi-Monte Carlo ages ago. (However, rather than the basic solution drafted in Table 2, lower variance solutions like systematic and residual sampling have been proposed in the particle literature and I wonder if any of these is a special form of quasi-Monte Carlo.) In the present setting, the authors move further and apply quasi-Monte Carlo to the particles themselves. However, they still assume the deterministic transform

\mathbf{x}_t^n = \Gamma_t(\mathbf{x}_{t-1}^n,\mathbf{u}_{t}^n)

which the q-block on which I stumbled each time I contemplated quasi-Monte Carlo… So the fundamental difficulty with the whole proposal is that the generation from the Markov proposal

m_t(\tilde{\mathbf{x}}_{t-1}^n,\cdot)

has to be of the above form. Is the strength of this assumption discussed anywhere in the paper? All baseline distributions there are normal. And in the case it does not easily apply, what would the gain bw in only using the second step (i.e., quasi-Monte Carlo-ing the multinomial simulation from the empirical cdf)? In a sequential setting with unknown parameters θ, the transform is modified each time θ is modified and I wonder at the impact on computing cost if the inverse cdf is not available analytically. And I presume simulating the θ’s cannot benefit from quasi-Monte Carlo improvements.

The paper obviously cannot get into every detail, obviously, but I would also welcome indications on the cost of deriving the Hilbert curve, in particular in connection with the dimension d as it has to separate all of the N particles, and on the stopping rule on m that means only Hm is used.

Another question stands with the multiplicity of low discrepancy sequences and their impact on the overall convergence. If Art Owen’s (1997) nested scrambling leads to the best rate, as implied by Theorem 7, why should we ever consider another choice?

In connection with Lemma 1 and the sequential quasi-Monte Carlo approximation of the evidence, I wonder at any possible Rao-Blackwellisation using all proposed moves rather than only those accepted. I mean, from a quasi-Monte Carlo viewpoint, is Rao-Blackwellisation easier and is it of any significant interest?

What are the computing costs and gains for forward and backward sampling? They are not discussed there. I also fail to understand the trick at the end of 4.2.1, using SQMC on a single vector instead of (t+1) of them. Again assuming inverse cdfs are available? Any connection with the Polson et al.’s particle learning literature?

Last questions: what is the (learning) effort for lazy me to move to SQMC? Any hope of stepping outside particle filtering?

not converging to London for an [extra]ordinary Read Paper

Posted in Books, Kids, pictures, Statistics, Travel, University life with tags , , , , , , , on November 21, 2014 by xi'an

London by Delta, Dec. 14, 2011On December 10, I will alas not travel to London to attend the Read Paper on sequential quasi-Monte Carlo presented by Mathieu Gerber and Nicolas Chopin to The Society, as I fly instead to Montréal for the NIPS workshops… I am quite sorry to miss this event, as this is a major paper which brings quasi-Monte Carlo methods into mainstream statistics. I will most certainly write a discussion and remind Og’s readers that contributed (800 words) discussions are welcome from everyone, the deadline for submission being January 02.

Relevant statistics for Bayesian model choice [hot off the press!]

Posted in Books, Statistics, University life with tags , , , , , , on October 30, 2014 by xi'an

jrssbabcOur paper about evaluating statistics used for ABC model choice has just appeared in Series B! It somewhat paradoxical that it comes out just a few days after we submitted our paper on using random forests for Bayesian model choice, thus bypassing the need for selecting those summary statistics by incorporating all statistics available and letting the trees automatically rank those statistics in term of their discriminating power. Nonetheless, this paper remains an exciting piece of work (!) as it addresses the more general and pressing question of the validity of running a Bayesian analysis with only part of the information contained in the data. Quite usefull in my (biased) opinion when considering the emergence of approximate inference already discussed on this ‘Og…

[As a trivial aside, I had first used fresh from the press(es) as the bracketted comment, before I realised the meaning was not necessarily the same in English and in French.]

RSS statistical analytics challenge 2014

Posted in Kids, R, Statistics, University life, Wines with tags , , , , on May 2, 2014 by xi'an

RSS_Challenge_2014Great news! The RSS is setting a data analysis challenge this year, sponsored by the Young Statisticians Section and Research Section of the Royal Statistical Society: Details are available on the wordpress website of the Challenge. Registration is open and the Challenge goes live on Tuesday 6 May 2014 for an exciting 6 weeks competition. (A wee bit of an unfortunate timing for those of us considering submitting a paper to NIPS!) Truly terrific, I have been looking for this kind of event to happen for many years (without finding the momentum to set it rolling…)  and hope it will generate a lot of exciting activity and replicas in other societies.

Series B news

Posted in Books, Statistics, University life with tags , , , , , , , , , , on January 24, 2014 by xi'an

IMG_2451IMG_2450The 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!

changing focus is not an option!

Posted in Statistics, University life with tags , , , , , , on October 11, 2013 by xi'an

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..!).

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