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 (Author), Soulier, Philippe (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	New York, NY : Springer New York : Imprint: Springer, 2020.
Edition:	1st ed. 2020.
Series:	Springer Series in Operations Research and Financial Engineering,
Subjects:	Probabilities. Statistics . Applied mathematics. Engineering mathematics. Probability Theory and Stochastic Processes. Statistical Theory and Methods. Applications of Mathematics. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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. .