## causal inference makes it to Stockholm

Posted in Statistics with tags , , , , , , , on October 12, 2021 by xi'an

Yesterday, Joshua Angrist and Guido Imbens, whose most cited paper is this JASA 1996 article with Don Rubin, were awarded the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for 2021. It is one of these not-so-rare instances when econometricians get this prize, with causality the motive for their award. I presume this will not see the number of Biometrika submissions involving causal inference go down! (Imbens wrote a book on causal inference with Don Rubin, and is currently editor of Econometrica. And Angrist wrote Mostly Harmless Econometrics, with J.S. Pischke, which I have not read.)

## congrats [IMS related]

Posted in Statistics with tags , , , , , , , , , , , on July 21, 2021 by xi'an

When I read through the June-July issue of the IMS Bulletin, I saw many causes for celebration and congratulations!, from Richard Samworth’s award of an Advanced ERC grant, to the new IMS fellows, including my friends, Ismael Castillo, Steve Mc Eachern, and Natesh Pillai, as well as my current or former associate editors, Johan Segers (JRSS B) and Changbao Wu (Biometrika). To my friends Alicia Carriquiry, David Dunson, and Tamara Broderick receiving 2021 COPSS awards, along others, including Wing Hung Wong (of the precursor Tanner & Wong, 1987 fame!). Natesh also figures among the “Quadfecta 23”, the exclusive club of authors having published at least one paper in each of the four Annals published by the IMS!

## ISBA 2021 low key

Posted in Kids, Mountains, pictures, Running, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , , , , , , , , , , , on July 2, 2021 by xi'an

Fourth day of ISBA (and ISB@CIRM), which was a bit low key for me as I had a longer hike with my wife in the morning, including a swim in a sea as cold as the Annecy lake last month!, but nonetheless enjoyable and crystal clear, then attacked my pile of Biometrika submissions that had accumulated beyond the reasonable since last week, chased late participants who hadn’t paid yet, reviewed a paper that was due two weeks ago, chatted with participants before they left, discussed a research problem, and as a result ended attending only four sessions over the whole day. Including one about Models and Methods for Networks and Graphs, with interesting computation challenges, esp. in block models, the session in memoriam of Hélène Massam, where Gérard Letac (part of ISB@CIRM!), Jacek Wesolowski, and Reza Mohammadi, all coauthors of Hélène, made presentations on their joint advances. Hélène was born in Marseille, actually, in 1949, and even though she did not stay in France after her École Normale studies, it was a further commemoration to attend this session in her birth-place. I also found out about them working on the approximation of a ratio of normalising constants for the G-Wishart. The last session of my data was the Susie Bayarri memorial lecture, with Tamara Roderick as the lecturer. Reporting on an impressive bunch of tricks to reduce computing costs for hierarchical models with Gaussian processes.

## conditioning an algorithm

Posted in Statistics with tags , , , , , , , , , , , on June 25, 2021 by xi'an

A question of interest on X validated: given a (possibly black-box) algorithm simulating from a joint distribution with density [wrt a continuous measure] p(z,y) (how) is it possible to simulate from the conditional p(y|z⁰)? Which reminded me of a recent paper by Lindqvist et al. on conditional Monte Carlo. Which zooms on the simulation of a sample X given the value of a sufficient statistic, T(X)=t, revolving about pivotal quantities and inversions à la fiducial statistics, following an earlier Biometrika paper by Lindqvist & Taraldsen, in 2005. The idea is to write

$X=\chi(U,\theta)\qquad T(X)=\tau(U,\theta)$

where U has a distribution that depends on θ, to solve τ(u,θ)=t in θ for a given pair (u,t) with solution θ(u,t) and to generate u conditional on this solution. But this requires getting “under the hood” of the algorithm to such an extent as not answering the original question, or being open to other solutions using the expression for the joint density p(z,y)… In a purely black box situation, ABC appears as the natural if approximate solution.

## Introduction to Sequential Monte Carlo [book review]

Posted in Books, Statistics with tags , , , , , , , , , , , , , , , , on June 8, 2021 by xi'an

[Warning: Due to many CoI, from Nicolas being a former PhD student of mine, to his being a current colleague at CREST, to Omiros being co-deputy-editor for Biometrika, this review will not be part of my CHANCE book reviews.]

My friends Nicolas Chopin and Omiros Papaspiliopoulos wrote in 2020 An Introduction to Sequential Monte Carlo (Springer) that took several years to achieve and which I find remarkably coherent in its unified presentation. Particles filters and more broadly sequential Monte Carlo have expended considerably in the last 25 years and I find it difficult to keep track of the main advances given the expansive and heterogeneous literature. The book is also quite careful in its mathematical treatment of the concepts and, while the Feynman-Kac formalism is somewhat scary, it provides a careful introduction to the sampling techniques relating to state-space models and to their asymptotic validation. As an introduction it does not go to the same depths as Pierre Del Moral’s 2004 book or our 2005 book (Cappé et al.). But it also proposes a unified treatment of the most recent developments, including SMC² and ABC-SMC. There is even a chapter on sequential quasi-Monte Carlo, naturally connected to Mathieu Gerber’s and Nicolas Chopin’s 2015 Read Paper. Another significant feature is the articulation of the practical part around a massive Python package called particles [what else?!]. While the book is intended as a textbook, and has been used as such at ENSAE and in other places, there are only a few exercises per chapter and they are not necessarily manageable (as Exercise 7.1, the unique exercise for the very short Chapter 7.) The style is highly pedagogical, take for instance Chapter 10 on the various particle filters, with a detailed and separate analysis of the input, algorithm, and output of each of these. Examples are only strategically used when comparing methods or illustrating convergence. While the MCMC chapter (Chapter 15) is surprisingly small, it is actually an introducing of the massive chapter on particle MCMC (and a teaser for an incoming Papaspiloulos, Roberts and Tweedie, a slow-cooking dish that has now been baking for quite a while!).