Overview
- Provides a comprehensive and self-contained overview of extreme value theory for time series
- Presents concise theoretical analysis of regular variation and weak convergence, with relation to time series
- Includes complete proofs and exercises with solutions
- Includes list of open problems to encourage future research?
Part of the book series: Springer Series in Operations Research and Financial Engineering (ORFE)
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Table of contents (16 chapters)
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Front Matter
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Time Series Models
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Front Matter
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Keywords
About this book
This book aims to present a comprehensive, self-contained, and concise overview of extreme value theory for time series, incorporating the latest research trends alongside classical methodology. Appropriate for graduate coursework or professional reference, the book requires a background in extreme value theory for i.i.d. data and basics of time series. Following a brief review of foundational concepts, it progresses linearly through topics in limit theorems and time series models while including historical insights at each chapter’s conclusion. Additionally, the book incorporates complete proofs and exercises with solutions as well as substantive reference lists and appendices, featuring a novel commentary on the theory of vague convergence.
Authors and Affiliations
About the authors
Rafal Kulik graduated from the University of Wroclaw, Poland. He is currently a Professor at the Department of Mathematics and Statistics, University of Ottawa. His research interests are centered around limit theorems for stochastic processes with temporal dependence.
Philippe Soulier graduated from Ecole Normale Supérieure de Paris and obtained his PhD at University Paris XI Orsay. He is Professor of Mathematics at University Paris Nanterre. His main themes of research are long memory processes and extreme value theory.
Bibliographic Information
Book Title: Heavy-Tailed Time Series
Authors: Rafal Kulik, Philippe Soulier
Series Title: Springer Series in Operations Research and Financial Engineering
DOI: https://doi.org/10.1007/978-1-0716-0737-4
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2020
Hardcover ISBN: 978-1-0716-0735-0Published: 02 July 2020
eBook ISBN: 978-1-0716-0737-4Published: 01 July 2020
Series ISSN: 1431-8598
Series E-ISSN: 2197-1773
Edition Number: 1
Number of Pages: XIX, 681
Number of Illustrations: 2 b/w illustrations, 5 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Statistical Theory and Methods, Applications of Mathematics