**T**wo of my colleagues [and co-authors] at Dauphine, Elyès Jouini and Clotilde Napp, published a paper in Science last week (and an associated tribune in Le Monde which I spotted first) about explaining differences in national gender inequalities in maths (as measured by PISA) in terms of the degree of overall inequality in the respective countries. Gaps in the highest maths performer sex ratio. While I have no qualm about the dependency or the overall statistical cum machine learning analysis (supported by our common co-author Jean-Michel Marin), and while I obviously know nothing about the topic!, I leisurely wonder at the cultural factor (which may also partly explain for the degree of inequality) when considering that the countries at the bottom of the above graphs are rather religious (and mostly catholic). I also find it most intriguing that the gender gap is consistently reversed when considering higher performer sex ratio for reading, because mastering the language should be a strong factor in power structures and hence differences therein should also lead to inequalities…

## Archive for regression

## gender gaps

Posted in Statistics, University life with tags gender gap, Le Monde, machine learning, Nature, PISA, PSL Research University, regression, religions, Science, tribune, Université Paris Dauphine on March 31, 2018 by xi'an## weapons of math destruction [book review]

Posted in Books, Kids, pictures, Statistics, University life with tags AIs, algorithms, artificial intelligence, Artificial Intelligence and Statistics, big data, data privacy, machine learning, mathematics, population prediction, quant, regression, Statistics, superintelligence, Wall Street, weapons of math destruction, zero hour contract on December 15, 2016 by xi'an **A**s I had read many comments and reviews about this book, including one by Arthur Charpentier, on Freakonometrics, I eventually decided to buy it from my Amazon Associate savings (!). With a strong a priori bias, I am afraid, gathered from reading some excerpts, comments, and the overall advertising about it. And also because the book reminded me of another quantic swan. Not to mention the title. After reading it, I am afraid I cannot tell my ascertainment has changed much.

“Models are opinions embedded in mathematics.” (p.21)

The core message of this book is that the use of algorithms and AI methods to evaluate and rank people is unsatisfactory and unfair. From predicting recidivism to fire high school teachers, from rejecting loan applications to enticing the most challenged categories to enlist for for-profit colleges. Which is indeed unsatisfactory and unfair. Just like using the h index and citation ranking for promotion or hiring. (The book mentions the controversial hiring of many adjunct faculty by KAU to boost its ranking.) But this conclusion is not enough of an argument to write a whole book. Or even to blame *mathematics* for the unfairness: as far as I can tell, mathematics has nothing to do with unfairness. Some analysts crunch numbers, produce a score, and then managers make poor decisions. The use of *mathematics* throughout the book is thus completely inappropriate, when the author means statistics, machine learning, data mining, predictive algorithms, neural networks, &tc. (OK, there is a small section on Operations Research on p.127, but I figure deep learning can bypass the maths.) Continue reading

## Hidden Markov mixtures of regression

Posted in Statistics with tags Bayesian Analysis, Brad Carlin, ISBA, Markov switching models, regression, semi-parametrics on December 1, 2009 by xi'an**I**t took the RSS feed of * Bayesian Analysis* to disappear from my screen—because the

*4(4) issue was completed—for me to spot this very nice paper by Matthew A. Taddy and Athanasios Kottas on Markov switching regression models. It reminds me of earlier papers of mine’s with Monica Billio and Alain Monfort, and with Merrilee Hurn and Ana Justel, on Markov switching and mixtures of regression, respectively. At that time, with Merrilee, we had in mind to extend mixtures of regressions to generalised linear mixtures of generalised linear models but never found the opportunity to concretise the model. The current paper goes much farther by using mixtures of Dirichlet priors, thus giving a semi-parametric flavour to the mixture of regressions. There is also an interesting application to fishery management.*

**Bayesian Analysis****T**his issue also includes an emotional postnote by Brad Carlin, who is now stepping down from being the* Bayesian Analysis* Editor-in-chief. Brad unreservedly deserves thanks for mentoring

*towards a wider audience and a stronger requirement on the papers being published in the journal. I think*

**Bayesian Analysis***now is a mainstream journal rather than the emanation of a society, albeit as exciting as ISBA! The electronic format adopted by*

**Bayesian Analysis***should be exploited further towards forums and on-line discussions of all papers, rather than singling out one paper by issue, and I am glad Brad agrees on this possible change of editorial policy. All the best to the new Editor-in-chief, Herbie Lee!*

**Bayesian Analysis**