In All Likelihood: Statistical Modelling and Inference Using Likelihood by Yudi PawitanIn All Likelihood: Statistical Modelling and Inference Using Likelihood by Yudi Pawitan

In All Likelihood: Statistical Modelling and Inference Using Likelihood

byYudi Pawitan

Paperback | January 4, 2013

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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 demandsof 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 orsemiparametric 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 currentlyavailable 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 mixedmodels, non parametric smoothing, robustness, the EM algorithm and empirical likelihood.
Yudi Pawitan is a Professor in the Department of Medical Epidemiology and Biostatistics at the Karolinska Institutet in Sweden.
Title:In All Likelihood: Statistical Modelling and Inference Using LikelihoodFormat:PaperbackDimensions:544 pages, 9.21 × 6.14 × 0.03 inPublished:January 4, 2013Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199671222

ISBN - 13:9780199671229


Table of Contents

1. Introduction2. Elements of likelihood inference3. More properties of the likelihood4. Basic models and simple applications5. Frequentist properties6. Modelling relationships: regression models7. Evidence and the likelihood principle8. Score function and Fisher information9. Large Sample Results10. Dealing with nuisance parameters11. Complex data structure12. EM Algorithm13. Robustness of likelihood specification14. Estimating equation and quasi-likelihood15. Empirical likelihood16. Likelihood of random parameters17. Random and mixed effects models18. Nonparametric smoothing

Editorial Reviews

"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 likelihoodschool." --Biometrics