Three-Toed Sloth

Output Summary

After long, long journeys, in one case going back to 2003, some papers have come out. Alphabetically by distinguished co-authors:

Aaron Clauset, CRS, and M. E. J. Newman, "Power-law distributions in empirical data", arxiv:0706.1062 = SIAM Review 51 (2009): 661--703

I wrote about this when we first submitted it. In the intervening two and a half years, many people have continued to make the baby Gauss cry by publishing, and publicizing, supposed power laws based on completely inadequate and unreliable methods. Because their methods are unsound, one has no idea whether they're right or not, short of re-analyzing the data properly. I sometimes imagine these authors singing

I could be right
I could be wrong
I feel nice when I sing this song

but many of them at least pretend to care about the truth of their claims, so I piously hope that in the fullness of time the community of inquirers will come around to using reliable methods. In which regard I am gratified, but also astonished, to see that this is already the most-cited paper I've contributed to, by such a large margin that it's unlikely anything else I do will ever rival it.

See also: Aaron.

Rob Haslinger, Kristina Klinkner and CRS, "The Computational Structure of Spike Trains", arxiv:1001.0036 = Neural Computation 22 (2010): 121--157

I haven't written about this one before, though I feel free to do so now that we're published. This was fun venture into applying state-reconstruction ideas, specifically CSSR, to neural spike trains, specifically the barrel cortex of the rat, which is it represents sensory input from the whiskers. (The experimentalists build special whisker-vibrating machines, which are actually quite impressive.) We do, I think, a pretty good job of predicting the spike trains in an entirely non-parametric way, and showing how their complexity is modulated by sensory stimuli — how much tweaking the whisker drives the cortical neuron.

CRS, "Dynamics of Bayesian Updating with Dependent Data and Misspecified Models", arxiv:0901.1342 = Electronic Journal of Statistics 3 (2009): 1039--1074

I also wrote about this when I first submitted it. I'm particularly grateful to one of the reviewers, who read the paper very carefully, totally got it, and provided many helpful suggestions, one of which grew into a new theorem on rates of convergence. Thank you, benevolent and thoughtful anonymous referee person! Also, the publication process at EJS was extremely fast and utterly painless.

Other output: my first hemi-demi-semi-co-supervised student graduating with his doctorate (a fine piece of work I wish I could link to); a paper draft finished and sitting on a collaborator's desk (no pressure!); the homophily paper is almost finished (I need to speed up some simulations and cut out most of the jokes); half-a-dozen referee reports of my own (a deliberate new low; made easier by boycotting Elsevier); five papers edited for Annals of Applied Statistics (a new high); nine lectures newly written or massively revised for 36-350; all the problem sets for 350 re-worked and much better; three books reviewed for American Scientist (and a whole bunch of mini-reviews for nowhere in particular).

On the other hand, no chapters finished for Statistical Analysis of Complex Systems; three very patient collaborators in different parts of Manhattan waiting for me to turn things around; one superhumanly patient collaborator in Santa Fe ditto; and one project which has been accreting since 2007 really needs to be cut and polished into some papers. Resolution for next year: more papers.

Self-Centered; Enigmas of Chance; Complexity; Power Laws; Minds, Brains, and Neurons