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Empirical Process Techniques for Dependent Data

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Cover of 'Empirical Process Techniques for Dependent Data'

Table of Contents

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    Book Overview
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    Chapter 1 Empirical Process Techniques for Dependent Data
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    Chapter 2 Weak Dependence: Models and Applications
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    Chapter 3 Maximal Inequalities and Empirical Central Limit Theorems
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    Chapter 4 On Hoeffding’s Inequality for Dependent Random Variables
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    Chapter 5 On the Coupling of Dependent Random Variables and Applications
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    Chapter 6 Empirical Processes of Residuals
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    Chapter 7 Asymptotic Expansion of the Empirical Process of Long Memory Moving Averages
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    Chapter 8 The Reduction Principle for the Empirical Process of a Long Memory Linear Process
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    Chapter 9 Distributional Limit Theorems for Empirical Processes Generated by Functions of a Stationary Gaussian Process
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    Chapter 10 Empirical Spectral Processes and Nonparametric Maximum Likelihood Estimation for Time Series
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    Chapter 11 Empirical Processes Techniques for the Spectral Estimation of Fractional Processes
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    Chapter 12 Tail Empirical Processes Under Mixing Conditions
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    Chapter 13 On the Bootstrap and Empirical Processes for Dependent Sequences
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    Chapter 14 Frequency Domain Bootstrap for Time Series
Attention for Chapter 9: Distributional Limit Theorems for Empirical Processes Generated by Functions of a Stationary Gaussian Process
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Chapter title
Distributional Limit Theorems for Empirical Processes Generated by Functions of a Stationary Gaussian Process
Chapter number 9
Book title
Empirical Process Techniques for Dependent Data
Published by
Birkhäuser, Boston, MA, January 2002
DOI 10.1007/978-1-4612-0099-4_9
Book ISBNs
978-1-4612-6611-2, 978-1-4612-0099-4
Authors

Miguel A. Arcones

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