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: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer New York
2020, 2020
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| Edition: | 1st ed. 2020 |
| Series: | Springer Series in Operations Research and Financial Engineering
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
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.