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Functional Gaussian Approximation for Dependent Structures | Oxford Scholarship Online
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Functional Gaussian Approximation for Dependent Structures

Florence Merlevède, Magda Peligrad, and Sergey Utev

Abstract

This book has its origin in the need for developing and analyzing mathematical models for phenomena that evolve in time and influence each another, and aims at a better understanding of the structure and asymptotic behavior of stochastic processes. This monograph has double scope. First, to present tools for dealing with dependent structures directed toward obtaining normal approximations. Second, to apply the normal approximations presented in the book to various examples. The main tools consist of inequalities for dependent sequences of random variables, leading to limit theorems, including ... More

Keywords: central limit theorem, invariance principles, maximal moment inequalities, dependent random variables, mixing sequences, martingale-like sequences, negative dependence

Bibliographic Information

Print publication date: 2019 Print ISBN-13: 9780198826941
Published to Oxford Scholarship Online: April 2019 DOI:10.1093/oso/9780198826941.001.0001

Authors

Affiliations are at time of print publication.

Florence Merlevède, author
Professor, Université Paris-Est Marne-La-Vallée

Magda Peligrad, author
Professor, University of Cincinnati

Sergey Utev, author
University of Leicester, Professor