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
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
