1. 1. Introduction. - 1. 2. Outer Integrals and Measurable Majorants. - 1. 3. Weak Convergence. - 1. 4. Product Spaces. - 1. 5. Spaces of Bounded Functions. - 1. 6. Spaces of Locally Bounded Functions. - 1. 7. The Ball Sigma-Field and Measurability of Suprema. - 1. 8. Hilbert Spaces. - 1. 9. Convergence: Almost Surely and in Probability. - 1. 10. Convergence: Weak Almost Uniform and in Probability. - 1. 11. Refinements. - 1. 12. Uniformity and Metrization. - 2. 1. Introduction. - 2. 2. Maximal Inequalities and Covering Numbers. - 2. 3. Symmetrization and Measurability. - 2. 4. Glivenko-Cantelli Theorems. - 2. 5. Donsker Theorems. - 2. 6. Uniform Entropy Numbers. - 2. 7. Bracketing Numbers. - 2. 8. Uniformity in the Underlying Distribution. - 2. 9. Multiplier Central Limit Theorems. - 2. 10. Permanence of the Donsker Property. - 2. 11. The Central Limit Theorem for Processes. - 2. 12. Partial-Sum Processes. - 2. 13. Other Donsker Classes. - 2. 14. Tail Bounds. - 3. 1. Introduction. - 3. 2. M-Estimators. - 3. 3. Z-Estimators. - 3. 4. Rates ofConvergence. - 3. 5. Random Sample Size Poissonization and Kac Processes. - 3. 6. The Bootstrap. - 3. 7. The Two-Sample Problem. - 3. 8. Independence Empirical Processes. - 3. 9. The Delta-Method. - 3. 10. Contiguity. - 3. 11. Convolution and Minimax Theorems. - A. Appendix. - A. 1. Inequalities. - A. 2. Gaussian Processes. - A. 2. 1. Inequalities and Gaussian Comparison. - A. 2. 2. Exponential Bounds. - A. 2. 3. Majorizing Measures. - A. 2. 4. Further Results. - A. 3. Rademacher Processes. - A. 4. Isoperimetric Inequalities for Product Measures. - A. 5. Some Limit Theorems. - A. 6. More Inequalities. - A. 6. 1. Binomial Random Variables. - A. 6. 2. Multinomial Random Vectors. - A. 6. 3. Rademacher Sums. - Notes. - References. - Author Index. - List of Symbols. Language: English