It has not been long since I read Reminiscences of a Statistician: The Company I Kept. I quoted this book and an arXiv paper here :see the posts. I became very grateful to him because of his contributions to the statistical science. I feel so sad to see his obituary, particularly when I’m soon going to have time for reading his books more carefully.

The Drake eq. is used as an indicator that the chance of finding an organic society equivalent to the human society. As you guess, such chance is extremely low. What would be a chance that two obituaries of eminent statisticians who influenced many can appear in the same bulletin. Personal thought led that the obituary section of the bulletin is further extreme than the Drake equation.

If you are an astronomer who are interested in spectral analysis and looked for statistical or data analysis literature, you cannot miss I.J.Good’s bump hunting paper.

Density Estimation and Bump-Hunting by the Penalized Likelihood Method Exemplified by Scattering and Meteorite Data
by I.J.Good and R.A. Gaskins in JASA, Vol.75, No. 369, pp. 42-56

The penalized likelihood approach for density estimation and bump hunting and its Bayesian interpretation has popularized statistical application to spectrum type natural science data.

Not by the popularity but by my personal interest in computational geometry and its statistical expansion, Worlsey’s publications became my reading list. Computational geometry pertains the goodness of nonparametric statistics for multivariate data which are not well explored compared to nonparametric methods for univariate data. His introductory paper about computational geometry like

Keith Worsley (1996)
The Geometry of Random images (zipped postscript), Chance, 9(1), pp.27-40

can be informative and useful to some astronomers.

Speaking of the Drake equation, it was the first thing that gave me a notion of probability, it describes how one would simply formulate and compute the chance of finding life beyond the earth. The equation is a process of constructing a likelihood function. In fact, I didn’t think this equation to be a likelihood function at that time but its unique creativity carved my memory. The way this equation describes how to compute the chance of the existence of extraterrestrial intelligence is a good example of chain rule in modifying likelihood functions.

I have never met those scholars face to face but through their writings, their works shaped my way of thinking. This personal experience made me hard to believe obituaries of two respectful statisticians. It was like getting estimates of the chance of meeting ETs which I found very small when I played with the equation. Although their chances are extremely low, things can happen. Finding life outside of the earth and finding a sad news of two eminent scientists’ death are alike.

Title: Statistical Models: Theory and Practice (click for the publisher’s website)
My one line review, rather a comment several months ago was

Bias in asymptotic standard errors is not a familiar topic for astronomers

and I don’t understand why I wrote it but I think I came up this comment owing to my pursuit of modeling measurement errors occurring in astronomical researches.

My overall impression of the book was that astronomers might not fancy it because of the cited examples and models quite irrelevant to astronomy. On the contrary, I liked it because it reflects what statistics ought to be in the real data analysis world. This does not mean the book covers every bit of statistics. When you teach statistics, you don’t expect student’s learning curve of statistical logistics is continuous. You only hope that they jump the discontinuity points successfully and you give every effort to lower the steps of these discontinuity points. The book looked to offering comforts to ease such efforts or to hint promises for almost continuous learning curves. The perspective and scope of the book was very impressive to me at that time.

It is sad to learn brilliant minded people passing away before their insights reach others who need them. I admire professors at Berkeley, not only because of their research activities and contributions but also because of their pedagogical contributions to statistics and its applications to many fields including astronomy (J. Neyman and E. Scott. are as familiar to statisticians as to astronomers, for example. Their papers about the spatial distribution of galaxies are, to my knowledge, well sought among astronomers).

He started his professional life as a probabilist and mathematical statistician with Bayesian leanings but became one of the world’s leading applied statisticians and a circumspect frequentist. In his words:

My own experience suggests that neither decision-makers nor their statisticians do in fact have prior probabilities. A large part of Bayesian statistics is about what you would do if you had a prior. For the rest, statisticians make up priors that are mathematically convenient or attractive. Once used, priors become familiar; therefore, they come to be accepted as ‘natural’ and are liable to be used again; such priors may eventually generate their own technical literature… Similarly, a large part of [frequentist] statistics is about what you would do if you had a model; and all of us spend enormous amounts of energy finding out what would happen if the data kept pouring in.

I have draft posts: one is about his book titled as Statistical Models: Theory and Practice and the other is about his article appeared in arXiv:stat not many months ago and now published in the American Statistician (TAS). In my opinion, both would help astronomers lowering the barrier of theoretical statistics, Bayesian and frequentist methods alike. I blame myself for delaying these posts. Carrying on one’s legacy, I believe, is easier while the person is alive.