Synopses & Reviews
This text concentrates on what can be achieved using the likelihood/Fisherian methods of taking into account uncertainty when studying a statistical problem. It takes the concept of the likelihood as the best method for unifying the demands of statistical modeling and theory of inference. Every likelihood concept is illustrated with realistic examples ranging from a simple comparison of two accident rates to complex studies that require generalized linear or semiparametric modeling. The emphasis is on likelihood not as just a device used to produce an estimate, but as an important tool for modeling.
Review
"[T]his book discusses using the likelihood function for both modeling and inference ... a nice introduction to a variety of topics ... this book can also serve as a good initial exposure to possibly new concepts without overwhelming [the reader] with details."--Technometrics
Review
"[T]his book discusses using the likelihood function for both modeling and inference ... a nice introduction to a variety of topics ... this book can also serve as a good initial exposure to possibly new concepts without overwhelming [the reader] with details."--
TechnometricsDescription
Includes bibliographical references (p. [503]-514) and index.
Table of Contents
1. Introduction
2. Elements of likelihood inference
3. More properites of the likelihood
4. Basic models and simple applications
5. Frequentist properties
6. Modelling relationship: regression models
7. Evidence and likelihood principles
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
Bibliography
Index