Readership: Advanced undergraduate students in statistics and mathematics.
Beginning graduate students in statistics and mathematics.
Traditionally-trained statisticians wanting to learn modern likelihood-based statistical methods.
Yudi Pawitan, Professor of Statistics, Department of Statistics, National University of Ireland, Cork
"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
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