**T**here is an Econometrics Summer Masterclass taking place in the department of economics next week in Warwick, on May 15, with Don Rubin as one of the speakers and the masterclass teacher.

## Archive for Don Rubin

## econometrics summer masterclass at Warwick, 15 May

Posted in pictures, Statistics, Travel, University life with tags causal inference, Don Rubin, economics, lock, summer course, University of Warwic on May 9, 2019 by xi'an## truth or truthiness [book review]

Posted in Books, Kids, pictures, Statistics, University life with tags Andrew Gelman, Cambridge University Press, causality, CHANCE, data science, Don Rubin, fracking, Howard Wainer, Oklahoma, testing, tribune, truthiness, University of Warwick on March 21, 2017 by xi'an**T**his 2016 book by Howard Wainer has been sitting (!) on my desk for quite a while and it took a long visit to Warwick to find a free spot to quickly read it and write my impressions. The subtitle is, as shown on the picture, *“Distinguishing fact from fiction by learning to think like a data scientist”*. With all due respect to the book, which illustrates quite pleasantly the dangers of (pseudo-)data mis- or over- (or eve under-)interpretation, and to the author, who has repeatedly emphasised those points in his books and ~~tribunes~~ opinion columns, including those in CHANCE, I do not think the book teaches how to think like a data scientist. In that an arbitrary neophyte reader would not manage to handle a realistic data centric situation without deeper training. But this collection of essays, some of which were tribunes, makes for a nice reading nonetheless.

I presume that in this post-truth and alternative facts [dark] era, the notion of *truthiness* is familiar to most readers! It is often based on a misunderstanding or a misappropriation of data leading to dubious and unfounded conclusions. The book runs through dozens of examples (some of them quite short and mostly appealing to common sense) to show how this happens and to some extent how this can be countered. If not avoided as people will always try to bend, willingly or not, the data to their conclusion.

There are several parts and several themes in Truth or Truthiness, with different degrees of depth and novelty. The more involved part is in my opinion the one about causality, with illustrations in educational testing, psychology, and medical trials. (The illustration about fracking and the resulting impact on Oklahoma earthquakes should not be in the book, except that there exist officials publicly denying the facts. The same remark applies to the testing cheat controversy, which would be laughable had not someone ended up the victim!) The section on graphical representation and data communication is less exciting, presumably because it comes *after* Tufte’s books and message. I also feel the 1854 cholera map of John Snow is somewhat over-exploited, since he only drew the map after the epidemic declined. The final chapter * Don’t Try this at Home* is quite anecdotal and at the same time this may the whole point, namely that in mundane questions thinking like a data scientist is feasible and leads to sometimes surprising conclusions!

*“In the past a theory could get by on its beauty; in the modern world, a successful theory has to work for a living.” (p.40)*

The book reads quite nicely, as a whole and a collection of pieces, from which class and talk illustrations can be borrowed. I like the “learned” tone of it, with plenty of citations and witticisms, some in Latin, Yiddish and even French. (Even though the later is somewhat inaccurate! *Si ça avait pu se produire, ça avait dû se produire* [p.152] would have sounded more vernacular in my Gallic opinion!) I thus enjoyed unreservedly Truth or Truthiness, for its rich style and critical message, all the more needed in the current times, and far from comparing it with a bag of potato chips as Andrew Gelman did, I would like to stress its classical tone, in the sense of being immersed in a broad and deep culture that seems to be receding fast.

## Approximate Maximum Likelihood Estimation

Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags ABC, Austria, Don Rubin, James Spall, Kiefer-Wolfowitz algorithm, Linz, optimisation, Peter Diggle, stochastic approximation, stochastic gradient on September 21, 2015 by xi'an**B**ertl *et al.* arXived last July a paper on a maximum likelihood estimator based on an alternative to ABC techniques. And to indirect inference. (One of the authors in *et al.* is Andreas Futschik whom I visited last year in Linz.) Paper that I only spotted when gathering references for a reading list on ABC… The method is related to the “original ABC paper” of Diggle and Gratton (1984) which, parallel to Rubin (1984), contains in retrospect the idea of ABC methods. The starting point is stochastic approximation, namely the optimisation of a function of a parameter θ when written as an expectation of a random variable Y, **E**[Y|θ], as in the Kiefer-Wolfowitz algorithm. However, in the case of the likelihood function, there is rarely an unbiased estimator and the authors propose instead to use a kernel density estimator of the density of the summary statistic. This means that, at each iteration of the Kiefer-Wolfowitz algorithm, two sets of observations and hence of summary statistics are simulated and two kernel density estimates derived, both to be applied to the observed summary. The sequences underlying the Kiefer-Wolfowitz algorithm are taken from (the excellent optimisation book of) Spall (2003). Along with on-the-go adaptation and convergence test.

The theoretical difficulty in this extension is however that the kernel density estimator is not unbiased and thus that, rigorously speaking, the validation of the Kiefer-Wolfowitz algorithm does not apply here. On the practical side, the need for multiple starting points and multiple simulations of pseudo-samples may induce considerable time overload. Especially if bootstrap is used to evaluate the precision of the MLE approximation. Besides normal and M/G/1 queue examples, the authors illustrate the approach on a population genetic dataset of Borneo and Sumatra orang-utans. With 5 parameters and 28 summary statistics. Which thus means using a kernel density estimator in dimension 28, a rather perilous adventure..!

## Statistics slides (5)

Posted in Books, Kids, Statistics, University life with tags Bayesian statistics, Don Rubin, HPD region, map, Paris, Université Paris Dauphine on December 7, 2014 by xi'an**H**ere is the fifth and last set of slides for my third year statistics course, trying to introduce Bayesian statistics in the most natural way and hence starting with… Rasmus’ socks and ABC!!! This is an interesting experiment as I have no idea how my students will react. Either they will see the point besides the anecdotal story or they’ll miss it (being quite unhappy so far about the lack of mathematical rigour in my course and exercises…). We only have two weeks left so I am afraid the concept will not have time to seep through!

## Statistics slides (4)

Posted in Books, Kids, Statistics, University life with tags asymptotics, Bayesian statistics, Don Rubin, EM algorithm, likelihood function, likelihood surface, missing values, Paris, score function, Université Paris Dauphine on November 10, 2014 by xi'an**H**ere is the fourth set of slides for my third year statistics course, trying to build intuition about the likelihood surface and why on Earth would one want to find its maximum?!, through graphs. I am yet uncertain whether or not I will reach the point where I can teach more asymptotics so maybe I will also include asymptotic normality of the MLE under regularity conditions in this chapter…

## Statistics slides (3)

Posted in Books, Kids, Statistics, University life with tags ABC, Bayesian statistics, bootstrap, Don Rubin, empirical cdf, Glivenko-Cantelli Theorem, Monte Carlo methods, Monte Carlo Statistical Methods, Paris, simulation, Université Paris Dauphine on October 9, 2014 by xi'an**H**ere is the third set of slides for my third year statistics course. Nothing out of the ordinary, but the opportunity to link statistics and simulation for students not yet exposed to Monte Carlo methods. (No ABC yet, but who knows?, I may use ABC as an entry to Bayesian statistics, following Don Rubin’s example! Surprising typo on the Project Euclid page for this 1984 paper, by the way…) On Monday, I had the pleasant surprise to see Shravan Vasishth in the audience, as he is visiting Université Denis Diderot (Paris 7) this month.