Calyampudi Radhakrishna Rao (1920-2023)

Just heard that C.R. Rao had passed away on Wednesday. Above is a 1941 picture I photographed while attending the jubilee of the Department of Statistics of the University of Calcuta. Showing R.A. Fisher and P.C. Mahalanobis surrounded by faculty and students from the Department. Including a very young Rao who would a few years later go to Cambridge and write a PhD thesis on ANOVA under the supervision of R.A. Fisher. While my own interactions with C.R. Rao have been quite limited, from attending a seminar dinner with him when he visited Purdue University in 1988, to writing a critical assessment of Pitman nearness that he reportedly disliked, to writing chapters in some of the handbooks he edited and a review paper on Rao-Blackwellisation for the International Statistical Review special issue for his 100th birthday (which almost coincided with mine, one day off), he stood as a giant of the field, having impacted statistics and beyond in many and profound ways. The Hindu published an obituary immediately after his death, while Current Science has a longer if older biography full of pictures and Significance a series of articles on “C.R. Rao’s Century”. However, I’d like to recall this quote of his’, acknowledging his mother for his work habits.

‘For instilling in me the quest for knowledge, I owe to my mother, A. Laxmikanthamma, who, in my younger days, woke me up every day at four in the morning and lit the oil lamp for me to study in the quiet hours of the morning when the mind is fresh.’

Advancements in Bayesian Methods and Implementations [to appear]

As noted in another post, I wrote a chapter on Bayesian testing for an incoming handbook, Advancements in Bayesian methods and implementations which is published by Elsevier at an atrocious price (as usual). Here is the table of contents:

1. Fisher Information, Cramèr-Rao and Bayesian Paradigm by Roy Frieden
2. Compound beta binomial distribution functions by Angelo Plastino
3. MCMC for GLMMS by Vivekananda Roy
4. Signal Processing and Bayesian by Chandra Murthy
5. Mathematical theory of Bayesian statistics where all models are wrong by Sumio Watanabe
6. Machine Learning and Bayesian by Jun Zhu
7. Non-parametric Bayes by Stephen Walker
8. [50 shades of] Bayesian testing [of hypotheses] by Christian P. Robert
9. Data Analysis with humans by Sumio Kaski
10. Bayesian Inference under selection by G. Alastair Young
10. Variational inference or Functional horseshoe by Anirban Bhattacharya
11. Generalized Bayes by Ryan Martin

and my chapter is also available on arXiv, quickly gathered from earlier short courses at O’Bayes meetings and some xianblog entries on the topic, hence not containing much novelty!

an elegant book [review]

“Handbook of Mixture Analysis is an elegant book on the mixture models. It covers not only statistical foundations but also extensions and applications of mixture models. The book consists of 19 chapters (each chapter is an independent paper), and collectively, these chapters weave into an elegant web of mixture models” Yen-Chi Chen (U. Washington)

Handbook of Mixture Analysis [cover]

On the occasion of my talk at JSM2018, CRC Press sent me the cover of our incoming handbook on mixture analysis, courtesy of Rob Calver who managed to get it to me on very short notice! We are about ready to send the manuscript to CRC Press and hopefully the volume will get published pretty soon. It would have been better to have it ready for JSM2018, but we editors got delayed by a few months for the usual reasons.

Statistical analyses using R

Another book I received from the Short Book Reviews section of the International Statistical Review is Everitt’s and Hothorn’s Handbook of statistical analyses using R. Here is a [blog-ified] version of my book review.

This book is the second (blue) edition of a successful (violet) handbook that can benefit a wide audience interested in using R for its data analysis. (After I wrote the review, I saw this appropriate analysis of the first edition.) It covers most of non-Bayesian statistical methods, with forays into exploratory data analysis with tools like principal components, clustering and bagging/boosting. As reflected in the long list of chapters, the coverage is quite extensive and only missing specialised statistical domains like time-series (apart from longitudinal data), econometrics (except for generalised linear models), and signal processing. Beside the absence of a Bayesian perspective (only mentioned in connection with BIC and the mclust package, while the Bayesian formalism would be a natural tool for analysing mixed models), I miss some material on simulation, the only entry found in the book being bootstrap (pages 153-154).

Given its title and emphasis on analyses, the book is logically associated with an R package HSAUR2 [if there is an intended pun, I missed it!] and works according to a fixed pattern: each chapter (1) starts with a description of a few datasets, (2) summarises the statistical main issues in one or two pages, and then (3) engages into an R analysis. As the complexity increases with the chapter number, the authors are relying more and more on specialised packages that need to be downloaded by the reader. I have no objection with this pedagogical choice, especially when considering that the packages are mostly recent. I would however have like a bit more details about those packages or at least about their main function, as the reader is left to experiment solely from the line of code provided in the handbook. (In contrast, a few passages are a bit “geeky” and require a deeper understanding of R objects than casual readers master. Also, using layout instead of par(mfrow=… is not that obvious.) My only criticism of the book at this level is the puzzling insistence on including all the datasets used therein in the form of tables. I frankly fail to see the point in spending so many pages on those tables given that they all are available from the HSAUR2 package. A page of further explanation, of background or of statistical theory would have been much more beneficial to any reader, in my opinion! The same criticism applies to the few exercises found at the end of each chapter. (The most glaring use of a table occurs in the graphical display chapter, of course! The authors rely on a dataset about the 50 north-American States and list the data instead of illustrating the use of a map….)

In conclusion, I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation is nice (with a single noticeable mishap on page 332 caused by Darwin’s tree of life.) It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small light-bulbs in the margin to point out features of R I was not aware of. (In particular, the authors mentioned the option type=”n” for plot that R-bloggers signalled as the most useful option for plotting.) In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) non-statisticians. (This post has also appeared on Statistical Forum on April 20.)