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Viser: In All Likelihood - Statistical Modelling and Inference Using Likelihood
In All Likelihood
Statistical Modelling and Inference Using Likelihood
Yudi Pawitan
(2013)
Sprog: Engelsk
Oxford University Press
520,00 kr.
6 stk på lager
Hvor er varen på lager?om ca. 2 hverdage
Detaljer om varen
- Undefined: 544 sider
- Udgiver: Oxford University Press (Januar 2013)
- ISBN: 9780199671229
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.
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
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