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In All Likelihood: Statistical Modelling and Inference Using Likelihood, by Yudi Pawitan
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Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from a simile comparison of two accident rates, to complex studies that require generalised linear or semiparametric modelling.
The emphasis is that the likelihood is not simply a device to produce an estimate, but an important tool for modelling. The book generally takes an informal approach, where most important results are established using heuristic arguments and motivated with realistic examples. With the currently available computing power, examples are not contrived to allow a closed analytical solution, and the book can concentrate on the statistical aspects of the data modelling. In addition to classical likelihood theory, the book covers many modern topics such as generalized linear models and mixed models, non parametric smoothing, robustness, the EM algorithm and empirical likelihood.
- Sales Rank: #113752 in Books
- Published on: 2013-03-01
- Released on: 2013-03-01
- Original language: English
- Number of items: 1
- Dimensions: 6.20" h x 1.20" w x 9.10" l, 1.80 pounds
- Binding: Paperback
- 544 pages
Review
"This is a splendid book with its contents thoroughly covering all likelihood ... Statements are firm, and explanations are full and clear. This book may be used as a reference work. It is strongly recommended as an academic library volume, and individually for statistics lecturers, advanced students, and researchers." --The Mathematical Gazette
"To those of us to whom it is a continuing irritation to be told that there are only two kinds of statisticians, freqentist and Bayesian, this book will come as an enormous relief ... a remarkable book, which deserves the widest distribution; I hope it will gain many converts to the likelihood school." --Biometrics
About the Author
Yudi Pawitan is a Professor in the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden
Most helpful customer reviews
47 of 48 people found the following review helpful.
Statistical Modelling and Inference for this Century
By Ingrid Baade
I used "In All Likelihood" as a basis for the 13 week 3rd year Mathematical Statistics/ Statistical Inference course I have almost finished teaching. Finding this book at Amazon was my very good fortune. It is exactly the way I would have tried to write such a course but I couldn't have done as good a job as Pawitan.
I like this book because it covers all the theory, such as, sufficiency, completeness, minimum variance unbiased estimation, large sample asymptotics etc. But the beauty of the book lies in the relevant, modern examples. Likelihood functions are liberally graphed for the many examples. These are created in R; if you are an R user, or wish to be, you'll like the availability of the source code. If you're not into R, it won't make a difference to the usability of the book.
Books like Bickel & Doksum, Casella & Berger and Rice, have the theory, but not the range of practical examples that add so much to "In All Likelihood". Pawitan's theoretical sections are comparatively easy to follow. Pawitan points out important results rather than the reader needing to surmise what bits of theory are useful in practice.
On the other hand, since reading Pawitan I can now read sections out of McCullough and Nelder, and other applications books, no longer feeling I have missed some important background theory.
I see signs of good teaching practice throughout "In All Likelihood" that make it easy to learn and teach from. For example, difficult concepts are often initially introduced in an example and then reintroduced in technical detail. This way the learner feels some familiarity the second time around.
Semester is nearly over. We covered the first nine chapters (out of 18) in 38 hours of lectures. I'm reading the rest of the book now. Every page or two something else I have heard, seen or read in the past begins to make more sense. Examples of topics in the second half of the book are the EM algorithm, Generalized Estimating Equations and random/mixed effects models. I told my students that if they considered buying a book for their future in statistics, "In All Likelihood" is a very good one.
10 of 10 people found the following review helpful.
Excellent book---well worth the money
By JVerkuilen
I first heard about this book in 2006 at the International Meeting of the Psychometric Society (in Montreal) from a colleague. I ordered it from Amazon the day I heard about it from one of the conference computers. It's great and I've found examples in it for teaching plus a lot of things that I simply didn't know, didn't remember, or didn't really understand the first time.
The examples make this book really useful compared to more technical texts like Bickel & Doksum or Lehmann. (These books are useful, of course, but not so much as texts for courses.) Pawitan's book has tons of really great little examples that bring the concepts down to earth for the reader. For instance, when he plots four score functions (normal, Poisson, binomial and Cauchy), you *see* immediately why estimation is more difficult in models such as the Cauchy compared to the normal. It also builds intuition about what the score function actually is. I have unpublished notes from John Marden (Statistics, UIUC), who was my statistical theory professor, which are very, very good. Pawitan's book is on par. The fact that the R code is available is fantastic.
5 of 5 people found the following review helpful.
Just a Great Book
By Dr. Charles Saunders
For a non-measure theoretic, applied stat text this is the best one you can buy - by far. Excellent examples (superb discussion of sufficiency and the invariance principle) and all the R Code. Great for self-study or the advanced undergrad/beginning grad classroom.
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