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Yudi Pawitan
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In All Likelihood: Statistical Modelling And Inference Using Likelihood Paperback – 1 March 2013

by Yudi Pawitan (Author)
4.7 out of 5 stars 27
See all formats and editions
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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.
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  1. ISBN-10
    0199671222
  2. ISBN-13
    978-0199671229
  3. Publisher
    Oxford University Press, Usa
  4. Publication date
    1 March 2013
  5. Language
    English
  6. Dimensions
    2.79 x 15.49 x 23.11 cm
  7. Print length
    542 pages
  8. See all details
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Product description

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

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

Product details

  • Publisher ‏ : ‎ Oxford University Press, Usa (1 March 2013)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 542 pages
  • ISBN-10 ‏ : ‎ 0199671222
  • ISBN-13 ‏ : ‎ 978-0199671229
  • Dimensions ‏ : ‎ 2.79 x 15.49 x 23.11 cm
  • Customer Reviews:
    4.7 out of 5 stars 27

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Yudi Pawitan

Yudi Pawitan

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Kieran M.
5.0 out of 5 stars Light Reading
Reviewed in the United Kingdom on 31 October 2021
Verified Purchase
Not for the faint hearted but helped me pass my resit on Likelihood Inference.
So 5/5 would recommended as a core textbook for anyone studying that
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MathsEngineer
5.0 out of 5 stars Modern, elegant, flexible, and nuanced approach to applied statistics
Reviewed in the United States on 15 January 2013
Verified Purchase
As a practicing data analyst, I frequently find the standard "classical" statistical techniques either useless, cumbersome, or convoluted for all but the most straightforward applications. However, before running across the book, I thought my only other option would be to utilize Bayesian methods (which are infinitely more flexible and elegant) to perform inference with complex, non-normal/non-linear models. Unfortunately, solving Bayesian models relies on two rather unappealing concepts: a prior probability (so that all your calculations still yield probabilities),and Markov Chain Monte Carlo (a computationally intensive, sometimes non-convergent, algorithm that is only asymptotically correct wrt the true Bayesian posterior probability distribution.)

Reading Dr. Pawitan's book introduced me to a very satisfying "third way" as he calls it. Instead of force-fitting all uncertainty into a probability, the "likelihood" approach recognizes two types of uncertainty, which is both novel in statistics and extremely refreshing once you understand why two types are necessary. The first, which I would call "well calibrated" uncertainty, is analogous to a confidence interval for the mean of a normal sample. With this type, we know how often we would be wrong under repeated sampling from this population, so we have a good idea how well our method brackets the true mean i.e., well calibrated.

The other type of uncertainty is unique to the likelihood approach. This type of uncertainty arises if precise, repeated sampling error rates cannot be derived or estimated. In this case you are left with basically two choices (apart from collecting more data): create asymptotically well-calibrated inferences (i.e., assume that as N -> infinity, your repeated-sampling probability statements would become more precise) or admit that you do not precisely know the error rate and then rely solely on the likelihood and perhaps some non-frequentist calibration metric (Dr. Pawitan shows how to do this using the AIC to "calibrate" the likelihood). This type of uncertainty is NOT stated in terms of probability, which I find incredibly honest, as giving probabilities gives the air of more accuracy/knowledge than is usually warranted with complex models.

From an applied standpoint, I think the likelihood approach is superior to the Bayesian approach not because it is necessarily more accurate, but because it possesses a far less cumbersome theoretical apparatus while retaining all the flexibility and elegance of a Bayesian approach. Likelihood methods do not require Markov Chain Monte Carlo, nor do they require jacobians for transformations on inferences - instead, all you need is a good old "root finder" to solve essentially all problems. Simulation is useful if you are doing bootstrapping along with likelihood, but it is not an essential part of the "inference machine" so to speak. For missing data, you can use Expectation Maximization, which again only requires a simple computer package. Finally, you can incorporate prior information (subjective opinion or objective/data) as a prior likelihood, which unlike the prior probability does not need to integrate to one (in Bayesian stats, you have to resort to "improper priors" and hope for the best for situations where you want to represent complete ignorance). Also, a prior likelihood is probably psychologically closer to what most of us do when we evaluate the prior plausibility of a hypothesis, as we usually aren't very good at estimating raw probabilities.

The actual book is very complete, with good coverage of the fundamental mathematical statistics. Sometimes he could be clearer about his motivation for a particular topic, but overall I found it an excellent applied statistics text with great theoretical underpinning. If you are looking for a modern, flexible, and nuanced approach to applied statistics, you can do no better than this book.
22 people found this helpful
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William Stevenson
5.0 out of 5 stars Combine Both Schools in Your Statistical Thinking!
Reviewed in the United Kingdom on 22 October 2013
Verified Purchase
I am not a practicing statistician, but I do read a lot of papers involving medical statistics. Well worth reading.
One person found this helpful
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JVerkuilen
5.0 out of 5 stars Excellent book---well worth the money
Reviewed in the United States on 13 February 2008
Verified Purchase
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.
16 people found this helpful
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klattery
5.0 out of 5 stars Best Book on Likelihood
Reviewed in the United States on 4 January 2017
Verified Purchase
Excellent coverage of likelihood, with significant depth. Explanations are very clear, but forewarned they are targeted at a higher level audience. I'm thinking advanced undergrad stats course or entry level grad textbook. This is a textbook with lots of math, but a writer who really wants you to understand. I especially appreciated the coverage of Score and Fisher Information matrix, both observed and empirical. I also thought the treatment of nuisance and random parameters was fantastic. The examples and R code on his website are also great addition to this book.
5 people found this helpful
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