On the last day of the IFCAM workshop in Bangalore, Marc Lavielle from INRIA presented a talk on mixed effects where he illustrated his original computer language Monolix. And mentioned that his CRC Press book on Mixed Effects Models for the Population Approach was out! (Appropriately listed as out on a 14th of July on amazon!) He actually demonstrated the abilities of Monolix live and on diabets data provided by an earlier speaker from Kolkata, which was a perfect way to start initiating a collaboration! Nice cover (which is all I saw from the book at this stage!) that maybe will induce candidates to write a review for CHANCE. Estimation of those mixed effect models relies on stochastic EM algorithms developed by Marc Lavielle and Éric Moulines in the 90’s, as well as MCMC methods.
Archive for book review
As promised, I got back to this book, Implementing reproducible research (after the pigeons had their say). I looked at it this morning while monitoring my students taking their last-chance R exam (definitely last chance as my undergraduate R course is not reconoduced next year). The book is in fact an edited collection of papers on tools, principles, and platforms around the theme of reproducible research. It obviously links with other themes like open access, open data, and open software. All positive directions that need more active support from the scientific community. In particular the solutions advocated through this volume are mostly Linux-based. Among the tools described in the first chapter, knitr appears as an alternative to sweave. I used the later a while ago and while I like its philosophy. it does not extend to situations where the R code within takes too long to run… (Or maybe I did not invest enough time to grasp the entire spectrum of sweave.) Note that, even though the book is part of the R Series of CRC Press, many chapters are unrelated to R. And even more [unrelated] to statistics.
This limitation is somewhat my difficulty with [adhering to] the global message proposed by the book. It is great to construct such tools that monitor and archive successive versions of code and research, as anyone can trace back the research steps conducting to the published result(s). Using some of the platforms covered by the book establishes for instance a superb documentation principle, going much further than just providing an “easy” verification tool against fraudulent experiments. The notion of a super-wiki where notes and preliminary versions and calculations (and dead ends and failures) would be preserved for open access is just as great. However this type of research processing and discipline takes time and space and human investment, i.e. resources that are sparse and costly. Complex studies may involve enormous amounts of data and, neglecting the notions of confidentiality and privacy, the cost of storing such amounts is significant. Similarly for experiments that require days and weeks of huge clusters. I thus wonder where those resources would be found (journals, universities, high tech companies, …?) for the principle to hold in full generality and how transient they could prove. One cannot expect the research time to garantee availability of those meta-documents for remote time horizons. Just as a biased illustration, checking the available Bayes’ notebooks meant going to a remote part of London at a specific time and with a preliminary appointment. Those notebooks are not available on line for free. But for how long?
“So far, Bob has been using Charlie’s old computer, using Ubuntu 10.04. The next day, he is excited to find the new computer Alice has ordered for him has arrived. He installs Ubuntu 12.04″ A. Davison et al.
Putting their principles into practice, the authors of Implementing reproducible research have made all chapters available for free on the Open Science Framework. I thus encourage anyone interesting in those principles (and who would not be?!) to peruse the chapters and see how they can benefit from and contribute to open and reproducible research.
While I had not had kamikaze pigeons hitting my windows for quite a while…, it may be that one of them decided to move to biological warfare: when I came back from Edinburgh, my office at the University was in a terrible state as a bird had entered through a tiny window opening and wrecked havoc on the room, dropping folders and rocks from my shelves and… leaving a most specific proof of its visit. This bird was particularly attracted by and aggressive against the above book, Implementing Reproducible Research, standing on top of my books to review for CHANCE. Obvious disclaimer: this reflects neither my opinion nor the University opinion about the book contents, but only the bird’s, which is solely responsible for its action!
My friends Randal Douc and Éric Moulines just published this new time series book with David Stoffer. (David also wrote Time Series Analysis and its Applications with Robert Shumway a year ago.) The books reflects well on the research of Randal and Éric over the past decade, namely convergence results on Markov chains for validating both inference in nonlinear time series and algorithms applied to those objects. The later includes MCMC, pMCMC, sequential Monte Carlo, particle filters, and the EM algorithm. While I am too close to the authors to write a balanced review for CHANCE (the book is under review by another researcher, before you ask!), I think this is an important book that reflects the state of the art in the rigorous study of those models. Obviously, the mathematical rigour advocated by the authors makes Nonlinear Time Series a rather advanced book (despite the authors’ reassuring statement that “nothing excessively deep is used”) more adequate for PhD students and researchers than starting graduates (and definitely not advised for self-study), but the availability of the R code (on the highly personal page of David Stoffer) comes to balance the mathematical bent of the book in the first and third parts. A great reference book!
When visiting the bookstore on the campus of the University of Warwick two weeks ago, I spotted this book, Philosophy of Science, a very short introduction, by Samir Okasha, and the “bargain” offer of getting two books for £10 enticed me to buy it along with a Friedrich Nietzsche, a very short introduction… (Maybe with the irrational hope that my daughter would take a look at those for her philosophy course this year!)
“Popper’s attempt to show that science can get by without induction does not succeed.” (p.23)
Since this is [unsusrprisingly!] a very short introduction, I did not get much added value from the book. Nonetheless, it was an easy read for short trips in the metro and short waits here and there. And would be a good [very short] introduction to any one newly interested in the philosophy of sciences. The first chapter tries to define what science is, with reference to the authority of Popper (and a mere mention of Wittgenstein), and concludes that there is no clear-cut demarcation between science and pseudo-science. (Mathematics apparently does not constitute a science: “Physics is the most fundamental science of all”, p.55) I would have liked to see the quote from Friedrich Nietzsche
“It is perhaps just dawning on five or six minds that physics, too, is only an interpretation and exegesis of the world (to suit us, if I may say so!) and not a world-explanation.”
in Beyond Good and Evil. as it illustrates the main point of the chapter and maybe the book that scientific theories can never be proven true, Plus, it is often misinterpreted as a anti-science statement by Nietzsche. (Plus, it links both books I bought!) Continue reading
“Indoctrinating children in proper environmental thought was a hallmark of the green movement.” M. Crichton, micro, p. ix
I believe I read most of Michael Crichton‘s novels and this posthumous version (completed by Richard Preston) is not very different in its style and pattern from the previous ones. micro delivers an efficient fast-paced techno-thriller that filled most of one afternoon when convalescing at home. In that respect, it fills its intended role. I however feel this is one of the weakest novels in that the technological and scientific background is very poor. (The best Crichton’s novels are in my opinion The Andromeda Strain and Airframe. One of the last novels, State of Fear, carries a very anti-environmentalist and climatoskeptic message similar to the above quote.)
“Perhaps the most important lesson to be learned by direct experience is that the natural world (…) represents a complex system and therefore we cannot understand it and we cannot predict its behavior. “ M. Crichton, micro, p. x
Indeed, the plot of micro is based on the assumption that there exists a technology that can miniaturise living and non-living objects to 1/100th of their original size without any short-term impact. I remember watching as a child Fantastic Voyage, where a miniaturised submarine goes inside a blood vessel to remove a tumor, and I sat in front of a neighbour’s TV, mesmerised by the idea more than by the (weak) plot. This was in the laste 60’s. I also remember a sci’fi’ book I read when a pre-teen, with a great cover, called The Forgotten Planet: nothing truly memorable, apart from the cover, but hey this was a 1954 book. Now, micro does not use a deeper theory to justify this miniaturisation and the remainder of the plot is just as weak: I cannot imagine 1/100th humans surviving more than a few minutes in a rain forest environment! The place is crawling with insects, all way faster and far more deadly than tiny humans with a pocket knife, but the heroes conveniently meet only one dangerous insect at a time, loosing only at most one member of the group each time (sorry for the spoiler!). (In fact, the earlier Prey was much better at involving nanotechnologies. ) The grad students are very charicaturesque as well, providing biological infodump at times when they should be frozen solid with fright. Provided they had not been eaten already. The final resolution of the thriller is just… grotesque! So wait until you are sick or recovering from being sick before embarking upon this micro and no so fantastic trip!
A few weeks ago, I received a copy of The Cartoon Introduction to Statistics by Grady Klein and Alan Dabney, send by their publisher, Farrar, Staus and Giroux from New York City. (Never heard of this publisher previously, but I must admit the aggregation of those three names sounds great!) As this was an unpublished version of the book, to appear in July 2013, I first assumed my copy was a draft version, with black and white drawings using limited precision graphics.. However, when checking the already published Cartoon Introduction to Economics, I realised this was the style of Grady Klein (as reflected below).
Thus, I have to assume this is how The Cartoon Introduction to Statistics will look like when published in July… Actually, I received later a second copy of the definitive version, so I can guarantee this is the case. (Funny enough, there is a supportive quote of the author of Naked Statistics on the back-cover!) I am quite perplexed by the whole project. First, I do not see how a newcomer to the field can learn better from a cartoon with an average four sentences per page than from a regular introductory textbook. Cartoons introduce an element of fun into the explanation, with jokes and (irrelevant) side stories, but they are also distracting as readers are not always in a position to know what matters and what does not. Second, as the drawings are done in a rough style, I find this increases the potential for confusion. For instance, the above cover reproduces an example linking the histogram of a sample of averages and the normal distribution. If a reader has never heard of histograms, I do not see how he or she could gather how they are constructed in practice. The width of the bags is related to the number of persons in each bag (50 random Americans) in the story, while it should be related to the inverse of the square root of this number in the theory. Similarly, I find the explanation about confidence intervals lacking: when trying to reassure the readers about the fact that any given random sample from a population might be misleading, the authors state that “in the long run most cans [of worms] have averages in the clump under the hump [of the normal pdf]“. This is not reassuring at all: when using confidence intervals based on 10 or on 10⁵ normal observations, the corresponding 95% confidence intervals on their mean both have 95% chances to contain the true mean. The long run aspect refers to the repeated use of those intervals. (I am not even mentioning the classical fallacy of stating that “we are 99.7% confident that the population average is somewhere between -1.73 and -0.27″…)
In conclusion, I remember buying an illustrated entry to Marx’ Das Kapital when I started economics in graduate school (as a minor). This gave me a very quick idea of the purpose of the book. However, I read through the whole book to understand (or try to understand) Marx’ analysis of the economy. And the introduction did not help much in this regard. In the present setting, we are dealing with statistics, not economics, not philosophy. Having read a cartoon about the average length of worms within a can of worms is not going to help much in understanding the Central Limit Theorem and the subsequent derivation of confidence intervals. The validation of statistical methods is done through mathematics, which provides a formal language cartoons cannot reproduce.