three decades back

Yesterday, I (we) found myself (ourselves) back in time, precisely 34 years ago, as we drove our daughter to Orly airport for her flight to Cayenne, French Guiana. And the start of her internship. Indeed, this is also the airport from which I left for Purdue University in 1987 and where my parents drove me then, more for sentimental reasons than out of necessity, as I had much less luggage than Rachel! This old airport has not changed that much, apart from the sharp increase in security restrictions. At the time, my parents were able to stay till the plane to Chicago left (two hours late) and watch it take off from the terraces of the airport. This time, we were just unable to enter the airport beyond the parking lot and watched the plane take off (one hour late) from a flight tracker! But overall there was the same bittersweet feeling of seeing one’s kid move (far) away for a major and exciting step in their professional life. (When I called my mom to watch for the plane flying west straight across Normandy, very close to our family roots, she reminded me of that and also of my grand-parents watching my plane flying by… Without a flight tracker! I actually remember spotting Mont Saint-Michel on that trip.) Fare well, Dr. R, and see you soon!
~

Metropolis-Hastings via Classification [One World ABC seminar]

Today, Veronika Rockova is giving a webinar on her paper with Tetsuya Kaji Metropolis-Hastings via classification. at the One World ABC seminar, at 11.30am UK time. (Which was also presented at the Oxford Stats seminar last Feb.) Please register if not already a member of the 1W ABC mailing list.

Metropolis-Hastings via classification

Veronicka Rockova (from Chicago Booth) gave a talk on this theme at the Oxford Stats seminar this afternoon. Starting with a survey of ABC, synthetic likelihoods, and pseudo-marginals, to motivate her approach via GANs, learning an approximation of the likelihood from the GAN discriminator. Her explanation for the GAN type estimate was crystal clear and made me wonder at the connection with Geyer’s 1994 logistic estimator of the likelihood (a form of discriminator with a fixed generator). She also expressed the ABC approximation hence created as the actual posterior times an exponential tilt. Which she proved is of order 1/n. And that a random variant of the algorithm (where the shift is averaged) is unbiased. Most interestingly requiring no calibration and no tolerance. Except indirectly when building the discriminator. And no summary statistic. Noteworthy tension between correct shape and correct location.

another electoral map

Statistics and Health Care Fraud & Measuring Crime [ASA book reviews]

From the recently started ASA books series on statistical reasoning in science and society (of which I already reviewed a sequel to The Lady tasting Tea), a short book, Statistics and Health Care Fraud, I read at the doctor while waiting for my appointment, with no chances of cheating! While making me realise that there is a significant amount of health care fraud in the US, of which I had never though of before (!), with possibly specific statistical features to the problem, besides the use of extreme value theory, I did not find me insight there on the techniques used to detect these frauds, besides the accumulation of Florida and Texas examples. As  such this is a very light introduction to the topic, whose intended audience of choice remains unclear to me. It is stopping short of making a case for statistics and modelling against more machine-learning options. And does not seem to mention false positives… That is, the inevitable occurrence of some doctors or hospitals being above the median costs! (A point I remember David Spiegelhalter making a long while ago, during a memorable French statistical meeting in Pau.) The book also illustrates the use of a free auditing software called Rat-stats for multistage sampling, which apparently does not go beyond selecting claims at random according to their amount. Without learning from past data. (I also wonder if the criminals can reduce the chances of being caught by using this software.)

A second book on the “same” topic!, Measuring Crime, I read, not waiting at the police station, but while flying to Venezia. As indicated by the title, this is about measuring crime, with a lot of emphasis on surveys and census and the potential measurement errors at different levels of surveying or censusing… Again very little on statistical methodology, apart from questioning the data, the mode of surveying, crossing different sources, and establishing the impact of the way questions are stated, but also little on bias and the impact of policing and preventing AIs, as discussed in Weapons of Math Destruction and in some of Kristin Lum’s papers.Except for the almost obligatory reference to Minority Report. The book also concludes on an history chapter centred at Edith Abbott setting the bases for serious crime data collection in the 1920’s.

[And the usual disclaimer applies, namely that this bicephalic review is likely to appear later in CHANCE, in my book reviews column.]