fake conference

One of my (former) master students approached me last week for support to attend an AI conference in London next May, as he had been invited there as a speaker with the prospect of publishing a paper in an AI journal. And very excited about it. As the letter of invitation definitely sounded fake to me and as Conference Series LLC did not seem connected to anything scientific, I had a quick check whether or not this was another instance of predatory conference and indeed the organisation is an outlet of the (in)famous OMICS International company. Setting conferences all around the year and all around the world by charging participants a significant amount and cramming all speakers on potentially any topic in the same room of a suburban motel (near Heathrow in that case). It is somewhat surprising that they still manage to capture victims but if they aim wide enough to cover students like the one who contacted me and had no idea of the possibility of such scams, no wonder the operation is still running. Coincidence, I was reading a news article in Nature, while in Seoul, that “South Korea’s education ministry wants to stop academics from participating in conferences that it considers “weak” and of little academic value”. I hope it works better than India’s earlier attempt at banning publications in predatory journals.

MASH in Le Monde

variance of an exponential order statistics

This afternoon, one of my Monte Carlo students at ENSAE came to me with an exercise from Monte Carlo Statistical Methods that I did not remember having written. And I thus “charged” George Casella with authorship for that exercise!

Exercise 3.3 starts with the usual question (a) about the (Binomial) precision of a tail probability estimator, which is easy to answer by iterating simulation batches. Expressed via the empirical cdf, it is concerned with the vertical variability of this empirical cdf. The second part (b) is more unusual in that the first part is again an evaluation of a tail probability, but then it switches to find the .995 quantile by simulation and produce a precise enough [to three digits] estimate. Which amounts to assess the horizontal variability of this empirical cdf.

As we discussed about this question, my first suggestion was to aim at a value of N, number of Monte Carlo simulations, such that the .995 x N-th spacing had a length of less than one thousandth of the .995 x N-th order statistic. In the case of the Exponential distribution suggested in the exercise, generating order statistics is straightforward, since, as suggested by Devroye, see Section V.3.3, the i-th spacing is an Exponential variate with rate (N-i+1). This is so fast that Devroye suggests simulating Uniform order statistics by inverting Exponential order statistics (p.220)!

However, while still discussing the problem with my student, I came to a better expression of the question, which was to figure out the variance of the .995 x N-th order statistic in the Exponential case. Working with the density of this order statistic however led nowhere useful. A bit later, after Google-ing the problem, I came upon this Stack Exchange solution that made use of the spacing result mentioned above, namely that the expectation and variance of the k-th order statistic are

which leads to the proper condition on N when imposing the variability constraint.

I am cold all over…

An email from one of my Master students who sent his problem sheet (taken from Monte Carlo Statistical Methods) late:

Bonsoir Professeur
Je « suis » votre cours du mercredi dont le formalisme mathématique me fait froid partout
Avec beaucoup de difficulté je vous envoie mes exercices du premier chapitre de votre livre.

which translates as

Good evening Professor,
I “follow” your Wednesday class which mathematical formalism makes me cold all over. With much hardship, I send you the first batch of problems from your book.

I know that winter is coming, but, still, making students shudder from mathematical cold is not my primary goal when teaching Monte Carlo methods!

[not] reading classics (#7)

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!]