Archive for Venezia

Venise n’est pas en Italie

Posted in pictures, Travel, University life with tags , , , , , on May 30, 2019 by xi'an

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

Posted in Books, Statistics with tags , , , , , , , , , , , , , , , , on May 7, 2019 by xi'an

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.]

La Fenice in blu, bianco e rosso

Posted in pictures, Running, Travel with tags , , , , , , , on April 21, 2019 by xi'an

back to Venice

Posted in Statistics with tags , , , , , , , on April 16, 2019 by xi'an

over the Alps [jatp]

Posted in Mountains, pictures, Travel with tags , , , , , , , , , on March 31, 2019 by xi'an

bentornato a Venezia!

Posted in Mountains, pictures, Running, Travel, University life with tags , , , , , , , on March 27, 2019 by xi'an

Roberto Casarin’s talk at CREST tomorrow

Posted in Statistics with tags , , , , , , , , , , , on March 13, 2019 by xi'an

My former student and friend Roberto Casarin (University Ca’Foscari, Venice) will talk tomorrow at the CREST Financial Econometrics seminar on

“Bayesian Markov Switching Tensor Regression for Time-varying Networks”

Time: 10:30
Date: 14 March 2019
Place: Room 3001, ENSAE, Université Paris-Saclay

Abstract : We propose a new Bayesian Markov switching regression model for multi-dimensional arrays (tensors) of binary time series. We assume a zero-inflated logit dynamics with time-varying parameters and apply it to multi-layer temporal networks. The original contribution is threefold. First, in order to avoid over-fitting we propose a parsimonious parameterisation of the model, based on a low-rank decomposition of the tensor of regression coefficients. Second, the parameters of the tensor model are driven by a hidden Markov chain, thus allowing for structural changes. The regimes are identified through prior constraints on the mixing probability of the zero-inflated model. Finally, we model the jointly dynamics of the network and of a set of variables of interest. We follow a Bayesian approach to inference, exploiting the Pólya-Gamma data augmentation scheme for logit models in order to provide an efficient Gibbs sampler for posterior approximation. We show the effectiveness of the sampler on simulated datasets of medium-big sizes, finally we apply the methodology to a real dataset of financial networks.