<p>Book: Self-Normalized Processes (2009)</p><p>authors: Victor H. de la Pena .  Tze Leung Lai . Qi-Man Shao</p><p>pages: 270</p><p>format: PDF</p><div class="Txt25" style="PADDING-TOP: 10px;">Table of contents</div><div><p>1. Introduction.- Part I Independent Random Variables.</p><p>2. Classical Limit Theorems and Preliminary Tools.</p><p>3. Self-Normalized Large Deviations</p><p>4. Weak Convergence of Self-Normalized Sums.</p><p>5. Stein’s Method and Self-Normalized Berry–Esseen Inequality.</p><p>6. Self-Normalized Moderate Deviations and Law of the Iterated Logarithm.</p><p>7. Cramér-type Moderate Deviations for Self-Normalized Sums.</p><p>8. Self-Normalized Empirical Processes and U-Statistics.</p><p>Part II Martingales and Dependent Random Vectors.</p><p>9. Martingale Inequalities and Related Tools.</p><p>10. A General Framework for Self-Normalization.</p><p>11. Pseudo-Maximization via Method of Mixtures.</p><p>12. Moment and Exponential Inequalities for Self-Normalized Processes.</p><p>13. Laws of the Iterated Logarithm for Self-Normalized Processes and Martingales.</p><p>14. Multivariate Matrix-Normalized Processes.</p><p>Part III Statistical Applications.</p><p>15. The t-Statistic and Studentized Statistics.</p><p>16. Self-Normalization and Approximate Pivots for Bootstrapping.</p><p>17. Self-Normalized Martingales and Pseudo-Maximization in Likelihood or Bayesian Inference.</p><p>18. Information Bounds and Boundary Crossing Probabilities for Self-Normalized Statistics in Sequential Analysis.- References.- Index</p><p> </p></div> <br/>

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