The AstroStat Slog

From IMS Bulletin Vol. 36(7) p.10, TerenceòÀÙs Stuff: Changing my mind (again)

Over the years IòÀÙve had many strong likes and dislikes for the various parts our subject. At different times I have confidently asserted this or that topic to be useless, wrong-headed, stupid, superficial, impossible, inappropriate, irrelevant, phony, boring, or finished. IòÀÙve been in love with sufficiency and hated cluster analysis. I thought the theory of games was elegant, while that of linear models lacked style. Group theory and invariance were fascinating to me, while maximum likelihood seemed mundane. Coordinate-free was the way to go, explicit parameters were to be avoided. Brownian theory was hot, sampling theory was not. The Markov property was natural, the mixing property artificial. Category theory was pure, applied probability wasnòÀÙt applied. Rao-Blackwellizing was cool, the delta method left me cold. Exact results were good, approximate ones bad. Scientific applications were beautiful, technological applications were ugly. Frequentist inference was objective, Bayesian inference subjective. And so it went on. My view was that means were to be avoided; extremes were the place to be.

IòÀÙve noticed another trend over my career. For decades I have jealously watched other people work on fascinating, complicated things — data, questions, contexts, models, methods and theory — leading them to fame and fortune, while I have been working on uninteresting, simple things, condemning myself to obscurity and poverty. I hasten to add that my things are always very interesting to me, and sometimes quite complicated too, just not to others. But the strange thing is that as time passed, many of those dimly-recalled, fascinating, complicated things from the past that others worked on, turn out to be just what I needed in order to answer a question at a later date. IòÀÙve been behind the times, but, at least in some cases, IòÀÙve caught up eventually.