a common confusion between sample and population moments

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!].

clustering dynamical networks

Yesterday I attended a presentation by Catherine Matias on dynamic graph structures, as she was giving a plenary talk at the 50th French statistical meeting, conveniently located a few blocks away from my office at ENSAE-CREST. In the nicely futuristic buildings of the EDF campus, which are supposed to represent cogs according to the architect, but which remind me more of these gas holders so common in the UK, at least in the past! (The E of EDF stands for electricity, but the original public company handled both gas and electricity.) This was primarily a survey of the field, which is much more diverse and multifaceted than I realised, even though I saw some recent developments by Antonietta Mira and her co-authors, as well as refereed a thesis on temporal networks at Ca’Foscari by Matteo Iacopini, which defence I will attend in early July. The difficulty in the approaches covered by Catherine stands with the amount and complexity of the latent variables induced by the models superimposed on the data. In her paper with Christophe Ambroise, she followed a variational EM approach. From the spectator perspective that is mine, I wondered at using ABC instead, which is presumably costly when the data size grows in space or in time. And at using tensor structures as in Mateo’s thesis. This reminded me as well of Luke Bornn’s modelling of basketball games following each player in real time throughout the game. (Which does not prevent the existence of latent variables.) But more vaguely and speculatively I also wonder at the meaning of the chosen models, which try to represent “everything” in the observed process, which seems doomed from the start given the heterogeneity of the data. While reaching my Keynesian pessimistic low- point, which happens rather quickly!, one could hope for projection techniques, towards reducing the dimension of the data of interest and of the parameter required by the model.

de.activated!