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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 13: Laws of the Iterated Logarithm for Self-Normalized Processes
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Citations

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Chapter title
Laws of the Iterated Logarithm for Self-Normalized Processes
Chapter number 13
Book title
Self-Normalized Processes
Published by
Springer, Berlin, Heidelberg, January 2009
DOI 10.1007/978-3-540-85636-8_13
Book ISBNs
978-3-54-085635-1, 978-3-54-085636-8
View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Professor > Associate Professor 1 33%
Student > Master 1 33%
Readers by discipline Count As %
Mathematics 2 67%
Engineering 1 33%

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