OXFORD UNIVERSITY PRESS

In All Likelihood: Statistical Modelling and Inference Using Likelihood

ISBN : 9780199671229

Price(incl.tax): 
¥8,305
Author: 
Yudi Pawitan
Pages
544 Pages
Format
Paperback
Size
173 x 233 mm
Pub date
Jan 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 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.

Index: 

1. Introduction
2. Elements of likelihood inference
3. More properties of the likelihood
4. Basic models and simple applications
5. Frequentist properties
6. Modelling relationships: regression models
7. Evidence and the likelihood principle
8. Score function and Fisher information
9. Large Sample Results
10. Dealing with nuisance parameters
11. Complex data structure
12. EM Algorithm
13. Robustness of likelihood specification
14. Estimating equation and quasi-likelihood
15. Empirical likelihood
16. Likelihood of random parameters
17. Random and mixed effects models
18. Nonparametric smoothing

About the author: 

Yudi Pawitan is a Professor in the Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden

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