Archive for the Books Category

the limits of R

Posted in Books, pictures, R, Statistics with tags , , , , , , , , , , , , on August 10, 2020 by xi'an

It has been repeated many times on many platforms, the R (or R⁰) number is not a great summary about the COVID-19 pandemic, see eg Rossman’s warning in The Conversation, but Nature chose to stress it one more time (in its 16 Jul edition). Or twice when considering a similar piece in Nature Physics. As Boris Johnson made it a central tool of his governmental communication policy. And some mayors started asking for their own local R numbers! It is obviously tempting to turn the messy and complex reality of this planetary crisis into a single number and even a single indicator R<1, but it is unhelpful and worse, from the epidemiology models being wrong (or at least oversimplifying) to the data being wrong (i.e., incomplete, biased and late), to the predictions being wrong (except for predicting the past). Nothing outrageous from the said Nature article, pointing out diverse degrees of uncertainty and variability and stressing the need to immediately address clusters rather than using the dummy R. As an aside, the repeated use of nowcasting instead of forecasting sounds like a perfect journalist fad, given that it does not seem to be based on a different model of infection or on a different statistical technique. (There is a nowcasting package in R, though!) And a wee bit later I have been pointed out at an extended discussion of an R estimation paper on Radford Neal’s blog.

Bayes @ NYT

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , , on August 8, 2020 by xi'an

A tribune in the NYT of yesterday on the importance of being Bayesian. When an epidemiologist. Tribune that was forwarded to me by a few friends (and which I missed on my addictive monitoring of the journal!). It is written by , a Canadian journalist writing about mathematics (and obviously statistics). And it brings to the general public the main motivation for adopting a Bayesian approach, namely its coherent handling of uncertainty and its ability to update in the face of new information. (Although it might be noted that other flavours of statistical analysis are also able to update their conclusions when given more data.) The COVID situation is a perfect case study in Bayesianism, in that there are so many levels of uncertainty and imprecision, from the models themselves, to the data, to the outcome of the tests, &tc. The article is journalisty, of course, but it quotes from a range of statisticians and epidemiologists, including Susan Holmes, whom I learned was quarantined 105 days in rural Portugal!, developing a hierarchical Bayes modelling of the prevalent  SEIR model, and David Spiegelhalter, discussing Cromwell’s Law (or better, humility law, for avoiding the reference to a fanatic and tyrannic Puritan who put Ireland to fire and the sword!, and had in fact very little humility for himself). Reading the comments is both hilarious (it does not take long to reach the point when Trump is mentioned, and Taleb’s stance on models and tails makes an appearance) and revealing, as many readers do not understand the meaning of Bayes’ inversion between causes and effects, or even the meaning of Jeffreys’ bar, |, as conditioning.

a conversation about eugenism at JSM

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , , , , on July 29, 2020 by xi'an

Following the recent debate on Fisher’s involvement in eugenics (and the renaming of the R.A. Fisher Award and Lectureship into the COPSS Distinguished Achievement Award and Lectureship), the ASA is running a JSM round table on Eugenics and its connections with statistics, to which I had been invited, along with Scarlett BellamyDavid Bellhouse, and David Cutler. The discussion is planned on 06 August at 3pm (ET, i.e., 7GMT) and here is the abstract:

The development of eugenics and modern statistical theory are inextricably entwined in history.  Their evolution was guided by the culture and societal values of scholars (and the ruling class) of their time through and including today.  Motivated by current-day societal reckonings of systemic injustice and inequity, this roundtable panel explores the role of prominent statisticians and of statistics more broadly in the development of eugenics at its inception and over the past century.  Leveraging a diverse panel, the discussions seek to shed light on how eugenics and statistics – despite their entangled past — have now severed, continue to have presence in ways that affect our lives and aspirations.

It is actually rather unclear to me why I was invited at the table, apart from my amateur interest in the history of statistics. On a highly personal level, I remember being introduced to Galton’s racial theories during my first course on probability, in 1982, by Prof Ogier, who always used historical anecdotes to enliven his lectures, like Galton trying to measure women mensurations during his South Africa expedition. Lectures that took place in the INSEE building, boulevard Adolphe Pinard in Paris, with said Adolphe Pinard being a founding member of the French Eugenics Society in 1913.

on the Internet nobody knows you are an old dog

Posted in Books, Kids, pictures with tags , , , , , , on July 25, 2020 by xi'an

[The Art of] Regression and other stories

Posted in Books, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , on July 23, 2020 by xi'an

CoI: Andrew sent me this new book [scheduled for 23 July on amazon] of his with Jennifer Hill and Aki Vehtari. Which I read in my garden over a few sunny morns. And as Andrew and Aki are good friends on mine, this review is definitely subjective and biased! Hence to take with a spoonful of salt.

The “other stories’ in the title is a very nice touch. And a clever idea. As the construction of regression models comes as a story to tell, from gathering and checking the data, to choosing the model specifications, to analysing the output and setting the safety lines on its interpretation and usages. I added “The Art of” in my own title as the exercise sounds very much like an art and very little like a technical or even less mathematical practice. Even though the call to the resident stat_glm R function is ubiquitous.

The style itself is very story-like, very far from a mathematical statistics book as, e.g., C.R. Rao’s Linear Statistical Inference and Its Applications. Or his earlier Linear Models which I got while drafted in the Navy. While this makes the “Stories” part most relevant, I also wonder how I could teach from this book to my own undergrad students without acquiring first (myself) the massive expertise represented by the opinions and advice on what is correct and what is not in constructing and analysing linear and generalised linear models. In the sense that I would find justifying or explaining opinionated sentences an amathematical challenge. On the other hand, it would make for a great remote course material, leading the students through the many chapters and letting them experiment with the code provided therein, creating new datasets and checking modelling assumptions. The debate between Bayesian and likelihood solutions is quite muted, with a recommendation for weakly informative priors superseded by the call for exploring the impact of one’s assumption. (Although the horseshoe prior makes an appearance, p.209!) The chapter on math and probability is somewhat superfluous as I hardly fathom a reader entering this book without a certain amount of math and stats background. (While the book warns about over-trusting bootstrap outcomes, I find the description in the Simulation chapter a wee bit too vague.) The final chapters about causal inference are quite impressive in their coverage but clearly require a significant amount of investment from the reader to truly ingest these 110 pages.

“One thing that can be confusing in statistics is that similar analyses can be performed in different ways.” (p.121)

Unsurprisingly, the authors warn the reader about simplistic and unquestioning usages of linear models and software, with a particularly strong warning about significance. (Remember Abandon Statistical Significance?!) And keep (rightly) arguing about the importance of fake data comparisons (although this can be overly confident at times). Great Chapter 11 on assumptions, diagnostics and model evaluation. And terrific Appendix B on 10 pieces of advice for improving one’s regression model. Although there are two or three pages on the topic, at the very end, I would have also appreciated a more balanced and constructive coverage of machine learning as it remains a form of regression, which can be evaluated by simulation of fake data and assessed by X validation, hence quite within the range of the book.

The document reads quite well, even pleasantly once one is over the shock at the limited amount of math formulas!, my only grumble being a terrible handwritten graph for building copters(Figure 1.9) and the numerous and sometimes gigantic square root symbols throughout the book. At a more meaningful level, it may feel as somewhat US centric, at least given the large fraction of examples dedicated to US elections. (Even though restating the precise predictions made by decent models on the eve of the 2016 election is worthwhile.) The Oscar for the best section title goes to “Cockroaches and the zero-inflated negative binomial model” (p.248)! But overall this is a very modern, stats centred, engaging and careful book on the most common tool of statistical modelling! More stories to come maybe?!