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Self-Normalized Processes

Overview of attention for book
Cover of 'Self-Normalized Processes'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Classical Limit Theorems, Inequalities and Other Tools
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    Chapter 3 Self-Normalized Large Deviations
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    Chapter 4 Weak Convergence of Self-Normalized Sums
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    Chapter 5 Stein's Method and Self-Normalized Berry–Esseen Inequality
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    Chapter 6 Self-Normalized Moderate Deviations and Laws of the Iterated Logarithm
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    Chapter 7 Cramér-Type Moderate Deviations for Self-Normalized Sums
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    Chapter 8 Self-Normalized Empirical Processes and U -Statistics
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    Chapter 9 Martingale Inequalities and Related Tools
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    Chapter 10 A General Framework for Self-Normalization
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    Chapter 11 Pseudo-Maximization via Method of Mixtures
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    Chapter 12 Moment and Exponential Inequalities for Self-Normalized Processes
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    Chapter 13 Laws of the Iterated Logarithm for Self-Normalized Processes
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    Chapter 14 Multivariate Self-Normalized Processes with Matrix Normalization
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    Chapter 15 The t -Statistic and Studentized Statistics
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    Chapter 16 Self-Normalization for Approximate Pivots in Bootstrapping
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    Chapter 17 Pseudo-Maximization in Likelihood and Bayesian Inference
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    Chapter 18 Sequential Analysis and Boundary Crossing Probabilities for Self-Normalized Statistics
Attention for Chapter 11: Pseudo-Maximization via Method of Mixtures
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