Pitman medal for Kerrie Mengersen

6831250-3x2-700x467My friend and co-author of many years, Kerrie Mengersen, just received the 2016 Pitman Medal, which is the prize of the Statistical Society of Australia. Congratulations to Kerrie for a well-deserved reward of her massive contributions to Australian, Bayesian, computational, modelling statistics, and to data science as a whole. (In case you wonder about the picture above, she has not yet lost the medal, but is instead looking for jaguars in the Amazon.)

This medal is named after EJG Pitman, Australian probabilist and statistician, whose name is attached to an estimator, a lemma, a measure of efficiency, a test, and a measure of comparison between estimators. His estimator is the best equivariant (or invariant) estimator, which can be expressed as a Bayes estimator under the relevant right Haar measure, despite having no Bayesian motivation to start with. His lemma is the Pitman-Koopman-Darmois lemma, which states that outside exponential families, sufficient is essentially useless (except for exotic distributions like the Uniform distributions). Darmois published the result first in 1935, but in French in the Comptes Rendus de l’Académie des Sciences. And the measure of comparison is Pitman nearness or closeness, on which I wrote a paper with my friends Gene Hwang and Bill Strawderman, paper that we thought was the final paper on the measure as it was pointing out several majors deficiencies with this concept. But the literature continued to grow after that..!

One Response to “Pitman medal for Kerrie Mengersen”

  1. Dan Simpson Says:

    I’ve known Kerrie my whole academic life, and the longer I go on, the more unfathomable she is. That she spins so many academic plates while juggling so many funding chainsaws and producing so many academic unicorns out of thin air is simply startling. That she does it without ever seeming overcome and while being exceptionally generous with her time and open to everyone Would be unbelievable if I didn’t constantly see it.

    There’s no one else even remotely like her and there aren’t enough accolades for people who can do what she does.

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