Xmas tree at UCL, with a special gift

Ph.D. students at UCL Statistics have made this Xmas tree out of bound and unbound volumes of statistics journals, not too hard to spot (especially the Current Indexes which I abandoned when I left my INSEE office a few years ago). An invisible present under the tree is the opening of several positions, namely two permanent lectureships and two three-year research fellowships, all in Statistics or Applied Probability, with the fellowship deadline being the 1st of December 2019!

running after my plane

A bit of a hectic trip to Abidjan last Sunday, starting from Caen in the early morning where I was supporting my daughter, wife, mother, and mother-in-law for the annual Rochambelle women-only 5k race on the previous evening! With my daughter managing a fantastic 52nd position and ending up first of her category! As I was driven to the local train station to get back to my 63kg of Annals and my plane, the on-going 10k race kept preventing us from reaching it and I eventually decided 8 minutes before the deadline to leave the car and race to the station, actually running along the 10k racers for one kilometer and managing to enter the train just before it was leaving (too bad I could not finish the race, this start would have made for a great time despite my current injury!). And then when I eventually reached the CDG airport with my 63kg, I was told my bags were not good enough to carry them and that I had to drop some years of these Annals in the bin! So very frustrating… At least the remaining books reached their intended destination.The plane ride itself was uneventful and above a constant cover of clouds. While catching up with some unread arXivals, I also watched a great 2018 Japanese movie, Shoplifters, which sounded more like a modern tale than a realistic story, so outlying its characters were. With unexpected revelations towards the end and overall a moving and very subtle reflection on what ultimately makes a family. (Reading reviews on the movie later made me realise one scene had been censored for plane audiences…)

off to Abidjan, with a few Annals [62kg of ’em]

the paper where you are a node

Sophie Donnet pointed out to me this arXived paper by Tianxi Li, Elizaveta Levina, and Ji Zhu, on a network resampling strategy for X validation, where I appear as a datapoint rather than as a [direct] citation! Which reminded me of the “where you are the hero” gamebooks with which my kids briefly played, before computer games took over. The model selection method is illustrated on a dataset made of X citations [reduced to 706 authors]  in all papers published between 2003 and 2012 in the Annals of Statistics, Biometrika, JASA, and JRSS Series B. With the outcome being the determination of a number of communities, 20, which the authors labelled as they wanted, based on 10 authors with the largest number of citations in the category. As it happens, I appear in the list, within the “mixed (causality + theory + Bayesian)” category (!), along with Jamie Robbins, Paul Fearnhead, Gilles Blanchard, Zhiqiang Tan, Stijn Vansteelandt, Nancy Reid, Jae Kwang Kim, Tyler VanderWeele, and Scott Sisson, which is somewhat mind-boggling in that I am pretty sure I never quoted six of these authors [although I find it hilarious that Jamie appears in the category, given that we almost got into a car crash together, at one of the Valencià meetings!].

same risk, different estimators

An interesting question on X validated reminded me of the epiphany I had some twenty years ago when reading a Annals of Statistics paper by Anirban Das Gupta and Bill Strawderman on shrinkage estimators, namely that some estimators shared the same risk function, meaning their integrated loss was the same for all values of the parameter. As indicated in this question, Stefan‘s instructor seems to believe that two estimators having the same risk function must be a.s. identical. Which is not true as exemplified by the James-Stein (1960) estimator with scale 2(p-2), which has constant risk p, just like the maximum likelihood estimator. I presume the confusion stemmed from the concept of completeness, where having a function with constant expectation under all values of the parameter implies that this function is constant. But, for loss functions, the concept does not apply since the loss depends both on the observation (that is complete in a Normal model) and on the parameter.