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Heavy-tailed time series

Kulik, Rafal.

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Heavy-tailed time series

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Heavy-tailed time series/ by Rafal Kulik, Philippe Soulier.
作者:	Kulik, Rafal.
其他作者:	Soulier, Philippe.
出版者:	New York, NY :Springer New York : : 2020.,
面頁冊數:	xix, 681 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Extreme value theory. -
電子資源:	https://doi.org/10.1007/978-1-0716-0737-4
ISBN:	9781071607374

Heavy-tailed time series
Kulik, Rafal.

Heavy-tailed time series[electronic resource] /by Rafal Kulik, Philippe Soulier. - New York, NY :Springer New York :2020. - xix, 681 p. :ill., digital ;24 cm. - Springer series in operations research and financial engineering,1431-8598. - Springer series in operations research and financial engineering..

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.

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.

ISBN: 9781071607374

Standard No.: 10.1007/978-1-0716-0737-4doiSubjects--Topical Terms:

646412
Extreme value theory.

LC Class. No.: QA273.6 / .K85 2020

Dewey Class. No.: 519.24

Heavy-tailed time series
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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.
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