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Cover: 9781071607350 | Heavy-Tailed Time Series | Philippe Soulier (u. a.) | Buch | xix
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Heavy-Tailed Time Series
Buch von Philippe Soulier (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor

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.

Zusammenfassung

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?

Inhaltsverzeichnis
Regular variation.- Regularly varying random variables.- Regularly varying random vectors.- Dealing with extremal independence.- Regular variation of series and random sums.- Regularly varying time series.- Limit theorems.- Convergence of clusters-. Point process convergence.- Convergence to stable and extremal processes.- The tall empirical and quantile processes.- Estimation of cluster functionals.- Estimation for extremally independent time series.- Bootstrap.- Time series models.- Max-stable processes.- Markov chains.- Moving averages.- Long memory processes.- Appendices.
Details
Erscheinungsjahr: 2020
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xix
681 S.
2 s/w Illustr.
5 farbige Illustr.
681 p. 7 illus.
5 illus. in color.
ISBN-13: 9781071607350
ISBN-10: 1071607359
Sprache: Englisch
Einband: Gebunden
Autor: Soulier, Philippe
Kulik, Rafal
Auflage: 1st edition 2020
Hersteller: Springer US
Springer New York
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 41 mm
Von/Mit: Philippe Soulier (u. a.)
Erscheinungsdatum: 02.07.2020
Gewicht: 1,337 kg
Artikel-ID: 118072801
Über den Autor

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.

Zusammenfassung

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?

Inhaltsverzeichnis
Regular variation.- Regularly varying random variables.- Regularly varying random vectors.- Dealing with extremal independence.- Regular variation of series and random sums.- Regularly varying time series.- Limit theorems.- Convergence of clusters-. Point process convergence.- Convergence to stable and extremal processes.- The tall empirical and quantile processes.- Estimation of cluster functionals.- Estimation for extremally independent time series.- Bootstrap.- Time series models.- Max-stable processes.- Markov chains.- Moving averages.- Long memory processes.- Appendices.
Details
Erscheinungsjahr: 2020
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: xix
681 S.
2 s/w Illustr.
5 farbige Illustr.
681 p. 7 illus.
5 illus. in color.
ISBN-13: 9781071607350
ISBN-10: 1071607359
Sprache: Englisch
Einband: Gebunden
Autor: Soulier, Philippe
Kulik, Rafal
Auflage: 1st edition 2020
Hersteller: Springer US
Springer New York
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 241 x 160 x 41 mm
Von/Mit: Philippe Soulier (u. a.)
Erscheinungsdatum: 02.07.2020
Gewicht: 1,337 kg
Artikel-ID: 118072801
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