This week, I decided not to report on the paper read at the Reading Classics student seminar, as it did not work out well-enough. The paper was the “Regression models and life-table” published in 1972 by David Cox… A classic if any! Indeed, I do not think posting a severe criticism of the presentation or the presentation itself would be of much use to anyone. It is rather sad as (a) the student clearly put some effort in the presentation, including a reproduction of an R execution, and (b) this was an entry on semi-parametrics, Kaplan-Meyer, truncated longitudinal data, and more, that could have benefited the class immensely. Alas, the talk did not take any distance from the paper, did not exploit the following discussion, and exceeded by far the allocated time, without delivering a comprehensible message. It is a complex paper with concise explanations, granted, but there were ways to find easier introductions to its contents in the more recent literature… It is possible that a second student takes over and presents her analysis of the paper next January. Unless she got so scared with this presentation that she will switch to another paper… [Season wishes to Classics Readers!]
Archive for classics
This week, thanks to a lack of clear instructions (from me) to my students in the Reading Classics student seminar, four students showed up with a presentation! Since I had planned for two teaching blocks, three of them managed to fit within the three hours, while the last one nicely accepted to wait till next week to present a paper by David Cox…
The first paper discussed therein was A new look at the statistical model identification, written in 1974 by Hirotugu Akaike. And presenting the AIC criterion. My student Rozan asked to give the presentation in French as he struggled with English, but it was still a challenge for him and he ended up being too close to the paper to provide a proper perspective on why AIC is written the way it is and why it is (potentially) relevant for model selection. And why it is not such a definitive answer to the model selection problem. This is not the simplest paper in the list, to be sure, but some intuition could have been built from the linear model, rather than producing the case of an ARMA(p,q) model without much explanation. (I actually wonder why the penalty for this model is (p+q)/T, rather than (p+q+1)/T for the additional variance parameter.) Or simulation ran on the performances of AIC versus other xIC’s…
The second paper was another classic, the original GLM paper by John Nelder and his coauthor Wedderburn, published in 1972 in Series B. A slightly easier paper, in that the notion of a generalised linear model is presented therein, with mathematical properties linking the (conditional) mean of the observation with the parameters and several examples that could be discussed. Plus having the book as a backup. My student Ysé did a reasonable job in presenting the concepts, but she would have benefited from this extra-week in including properly the computations she ran in R around the glm() function… (The definition of the deviance was somehow deficient, although this led to a small discussion during the class as to how the analysis of deviance was extending the then flourishing analysis of variance.) In the generic definition of the generalised linear models, I was also reminded of the
generality of the nuisance parameter modelling, which made the part of interest appear as an exponential shift on the original (nuisance) density.
The third paper, presented by Bong, was yet another classic, namely the FDR paper, Controlling the false discovery rate, of Benjamini and Hochberg in Series B (which was recently promoted to the should-have-been-a-Read-Paper category by the RSS Research Committee and discussed at the Annual RSS Conference in Edinburgh four years ago, as well as published in Series B). This 2010 discussion would actually have been a good start to discuss the paper in class, but Bong was not aware of it and mentioned earlier papers extending the 1995 classic. She gave a decent presentation of the problem and of the solution of Benjamini and Hochberg but I wonder how much of the novelty of the concept the class grasped. (I presume everyone was getting tired by then as I was the only one asking questions.) The slides somewhat made it look too much like a simulation experiment… (Unsurprisingly, the presentation did not include any Bayesian perspective on the approach, even though they are quite natural and emerged very quickly once the paper was published. I remember for instance the Valencia 7 meeting in Teneriffe where Larry Wasserman discussed about the Bayesian-frequentist agreement in multiple testing.)
This week at the Reading Classics student seminar, Thomas Ounas presented a paper, Statistical inference on massive datasets, written by Li, Lin, and Li, a paper out of The List. (This paper was recently published as Applied Stochastic Models in Business and Industry, 29, 399-409..) I accepted this unorthodox proposal as (a) it was unusual, i.e., this was the very first time a student made this request, and (b) the topic of large datasets and their statistical processing definitely was interesting even though the authors of the paper were unknown to me. The presentation by Thomas was very power-pointish (or power[-point]ful!), with plenty of dazzling transition effects… Even including (a) a Python software replicating the method and (b) a nice little video on internet data transfer protocols. And on a Linux machine! Hence the experiment was worth the try! Even though the paper is a rather unlikely candidate for the list of classics… (And the rendering in static power point no so impressive. Hence a video version available as well…)
The solution adopted by the authors of the paper is one of breaking a massive dataset into blocks so that each fits into the computer(s) memory and of computing a separate estimate for each block. Those estimates are then averaged (and standard-deviationed) without a clear assessment of the impact of this multi-tiered handling of the data. Thomas then built a software to illustrate this approach, with mean and variance and quantiles and densities as quantities of interest. Definitely original! The proposal itself sounds rather basic from a statistical viewpoint: for instance, evaluating the loss in information due to using this blocking procedure requires repeated sampling, which is unrealistic. Or using solely the inter-variance estimates which seems to be missing the intra-variability. Hence to be overly optimistic. Further, strictly speaking, the method does not asymptotically apply to biased estimators, hence neither to Bayes estimators (nor to density estimators). Convergence results are thus somehow formal, in that the asymptotics cannot apply to a finite memory computer. In practice, the difficulty of the splitting technique is rather in breaking the data into blocks since Big Data is rarely made of iid observations. Think of amazon data, for instance. A question actually asked by the class. The method of Li et al. should also include some boostrapping connection. E.g., to Michael’s bag of little bootstraps.
Today was my last Reading Seminar class and the concluding paper chosen by the student was Tukey’s “The future of data analysis“, a 1962 Annals of Math. Stat. paper. Unfortunately, reading this paper required much more maturity and background than the student could afford, which is the reason why this last presentation is not posted on this page… Given the global and a-theoretical perspective of the paper, it was quite difficult to interpret without further delving into Tukey’s work and without a proper knowledge of what was Data Analysis in the 1960’s. (The love affair of French statisticians with data analysis was then at its apex, but it has very much receded since then!) Being myself unfamiliar with this paper, and judging mostly from the sentences pasted by the student in his slides, I cannot tell how much of the paper is truly visionary and how much is cheap talk: focussing on trimmed and winsorized means does not sound like offering a very wide scope for data analysis… I liked the quote “It’s easier to carry a slide rule than a desk computer, to say nothing of a large computer”! (As well as the quote from Azimov “The sound of panting“…. (Still, I am unsure I will keep the paper within the list next year!)
Overall, despite a rather disappointing lower tail of the distribution of the talks, I am very happy with the way the seminar proceeded this year and the efforts produced by the students to assimilate the papers, the necessary presentation skills including building a background in LaTeX and Beamer for most students. I thus think almost all students will pass this course and do hope those skills will be profitable for their future studies…
Today’s classics seminar was rather special as two students were scheduled to talk. It was even more special as both students had picked (without informing me) the very same article by Berger and Sellke (1987), Testing a point-null hypothesis: the irreconcilability of p-values and evidence, on the (deep?) discrepancies between frequentist p-values and Bayesian posterior probabilities. In connection with the Lindley-Jeffreys paradox. Here are Amira Mziou’s slides:
and Jiahuan Li’s slides:
It was a good exercise to listen to both talks, seeing two perspectives on the same paper, and I hope the students in the class got the idea(s) behind the paper. As you can see, there were obviously repetitions between the talks, including the presentation of the lower bounds for all classes considered by Jim Berger and Tom Sellke, and the overall motivation for the comparison. Maybe as a consequence of my criticisms on the previous talk, both Amira and Jiahuan put some stress on the definitions to formally define the background of the paper. (I love the poetic line: “To prevent having a non-Bayesian reality”, although I am not sure what Amira meant by this…)
I like the connection made therein with the Lindley-Jeffreys paradox since this is the core idea behind the paper. And because I am currently writing a note about the paradox. Obviously, it was hard for the students to take a more remote stand on the reason for the comparison, from questioning .the relevance of testing point null hypotheses and of comparing the numerical values of a p-value with a posterior probability, to expecting asymptotic agreement between a p-value and a Bayes factor when both are convergent quantities, to setting the same weight on both hypotheses, to the ad-hocquery of using a drift on one to equate the p-value with the Bayes factor, to use specific priors like Jeffreys’s (which has the nice feature that it corresponds to g=n in the g-prior, as discussed in the new edition of Bayesian Core). The students also failed to remark on the fact that the developments were only for real parameters, as the phenomenon (that the lower bound on the posterior probabilities is larger than the p-value) does not happen so universally in larger dimensions. I would have expected more discussion from the ground, but we still got good questions and comments on a) why 0.05 matters and b) why comparing p-values and posterior probabilities is relevant. The next paper to be discussed will be Tukey’s piece on the future of statistics.