Archive for handbook

Handbook of Mixture Analysis [cover]

Posted in Books, Statistics, University life with tags , , , , , , , , on August 15, 2018 by xi'an

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

Posted in Books, R, Statistics with tags , , , , , on April 22, 2011 by xi'an

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.)

Typo in mixture survey

Posted in Books, Statistics with tags , , , on September 19, 2010 by xi'an

This morning I received the following email

(…) I have a question regarding an algorithm in one of your papers, “Bayesian Modelling and Inference on Mixtures of Distributions“.  On page 33, in the Metropolis-Hastings algorithm for the mixture you accept the proposal if r < u.  As I understand the MH algorithm you accept the proposal with probability r (technically min(r,1)), so I would expect that you accept if u < r.  I cannot see or find a reason elsewhere why r < u works?  If you could clarify why r < u works for the MH algorithm I would really appreciate it. (…)

which rightly points out an embarrassing typo in our mixture survey, published in the Handbook of Statistics, volume 25. Indeed, the inequality should be the reverse, u<r, as in the other algorithmic boxes of the survey.

Another handbook chapter

Posted in Books, Statistics with tags , , , , , on February 11, 2010 by xi'an

As I have received over the past semester half a dozen requests for contributing chapters in different handbooks, I wrote several rather similar introductions to Bayesian statistics and/or to computational statistics. Here is one for an Handbook of Statistical Systems Biology edited by D. Balding, M. Stumpf, and M. Girolami, to be published by Wiley. It is mostly inspired from the second chapter of Bayesian Core so it is not particularly novel. If I find some extra time within the coming months, I will also include a section on nonparametric Bayes… Before, I also have to write a revised edition to my chapter Bayesian Computational Methods in the Handbook of Computational Statistics (selling at an outrageous price, like most handbooks!), edited by J. Gentle, W. Härdle and Y. Mori.

Short introduction to Bayesian analysis

Posted in Books, Statistics, University life with tags , , , , on January 27, 2010 by xi'an

With Judith Rousseau, we wrote this short introduction to Bayesian data analysis for an incoming handbook on risk analysis, Bayesian methods and expert elicitation, with nothing really new there, just an hopefully nice summary. And hopefully useful for risk analysts! Here is the abstract: This introduction to Bayesian statistics presents the main concepts as well as the principal reasons advocated in favour of a Bayesian modelling. We cover the various approaches to prior determination as well as the basis asymptotic arguments in favour of using Bayes estimators. The testing aspects of Bayesian inference are also examined in details.


Posted in Books, Statistics with tags , , , , , , on September 3, 2009 by xi'an

Having been asked to fill an entry on Monte Carlo for the incoming International Handbook of Statistical Sciences, I obliged by writing a short piece, whose utility is rather limited. Indeed, I am afraid I am not very much convinced of the use of such encyclopedias, as they try to provide entries on about “everything” but end up being partial, quickly obsolete, and not so informative… This may sound overly negative, but I never ever use this kind of books for my research or my teaching, so I wonder who does. Encyclopedias (encyclopedii?) were fine when the “whole” of Science could be crammed in three dozen volumes with a slow enough updating process. Current students most likely check on Wikipedia or at large on the Web and, given the price of those behemoths, it seems only libraries can afford them, and then this may be wasted money anyway! My misgivings actually extend to contributed volumes that are (were?) fairly common, mostly in connection with one conference or another, and whose utility is rarely demonstrated. It takes highly dedicated editors to turn contributed volumes into useful coherent books, one example being the 1996 MCMC in Practice book deeply edited by Wally Gilks, Sylvia Richardson and David Spiegelhalter, that still serves as a reference for MCMC methods. Even the traditional Valencia volumes, while giving a snapshot of the on-going research in Bayesian statistics, along with a collection of discussions, have lost some of their luster, when compared with the impact of a discussion paper in Bayesian Analysis… Having conference papers submitted to Bayesian Analysis and selected on the same basis as regular papers, as was done for Valencia 8, seems to me a much better idea than publishing separately a rather costly volume, not read by enough people. (The dates and locations of Valencia 9, the last of the kind, have been announced. It will be in Benidorm, on June 3-8, 2010.)