Heavy-Tailed Time Series

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...

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Bibliographic Details
Main Authors: Kulik, Rafal, Soulier, Philippe (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2020, 2020
Edition:1st ed. 2020
Series:Springer Series in Operations Research and Financial Engineering
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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100 1 |a Kulik, Rafal 
245 0 0 |a Heavy-Tailed Time Series  |h Elektronische Ressource  |c by Rafal Kulik, Philippe Soulier 
250 |a 1st ed. 2020 
260 |a New York, NY  |b Springer New York  |c 2020, 2020 
300 |a XIX, 681 p. 7 illus., 5 illus. in color  |b online resource 
505 0 |a 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. 
653 |a Applied mathematics 
653 |a Engineering mathematics 
653 |a Statistical Theory and Methods 
653 |a Statistics  
653 |a Applications of Mathematics 
653 |a Probability Theory and Stochastic Processes 
653 |a Probabilities 
700 1 |a Soulier, Philippe  |e [author] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
490 0 |a Springer Series in Operations Research and Financial Engineering 
856 4 0 |u https://doi.org/10.1007/978-1-0716-0737-4?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 519.2 
520 |a 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