Models and likelihood are the backbone of modern statistics and data analysis. The coverage is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics. Anthony Davison blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practicioners. Its comprehensive coverage makes this the standard text and reference in the subject.

1 Introduction

2 Variation

3 Uncertainty

4 Likelihood

5 Models

6 Stochastic Models

7 Estimation and Hypothesis Testing

8 Linear Regression Models

9 Designed Experiments

10 Nonlinear Regression Models

11 Bayesian Models

12 Conditional and Marginal Inference

APPENDIX A: Practicals