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...
| Main Authors: | , |
|---|---|
| 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 |
| Summary: | 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 |
|---|---|
| Physical Description: | XIX, 681 p. 7 illus., 5 illus. in color online resource |
| ISBN: | 9781071607374 |