Applications of empirical process theory

Preface Reading guide 1. Introduction 2. Notations and definitions 3. Uniform laws of large numbers 4. First applications: consistency 5. Increments of empirical processes 6. Central limit theorems 7. Rates of convergence for maximum likelihood estimators 8. The non-i.i.d. case 9. Rates of convergence for least squares estimators 10. Penalties and sieves 11. Some applications to semi-parametric models 12. M-estimators Appendix References Author index Subject index List of symbols.