Stable Non-Gaussian Random Processes
Stochastic Models with Infinite Variance
Published on 22. November 2017
632 pages
978-1-351-41480-7 (ISBN)
Description
This book serves as a standard reference, making this area accessible not only to researchers in probability and statistics, but also to graduate students and practitioners. The book assumes only a first-year graduate course in probability. Each chapter begins with a brief overview and concludes with a wide range of exercises at varying levels of difficulty. The authors supply detailed hints for the more challenging problems, and cover many advances made in recent years.
Reviews / Votes
"There has been a pressing need for a book on this subject...The authors have succeeded in filling the gap...I am very glad a standard reference about stable processes now exists."- Bulletin of the London Mathematical Society
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
File size
13,02 MB
ISBN-13
978-1-351-41480-7 (9781351414807)
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Schweitzer Classification
Other editions
Additional editions
Gennady Samoradnitsky | M.S. Taqqu
Stable Non-Gaussian Random Processes
Stochastic Models with Infinite Variance
Book
06/1994
1st Edition
Chapman & Hall/CRC
€256.50
Shipment within 10-20 days
Persons
Samoradnitsky, Gennady
Content
1. Stable random variables on the real line 2. Multivariate stable distributions 3. Stable random processes and stochastic integrals 4. Dependence Structures of Multivariate Stable Distributions 5. Non-linear regression 6. Complex stable stochastic integrals and harmonizable processes 7. Self-similar processes 8. Chentsov random fields 9. Introduction to sample path properties 10. Boundedness, continuity and oscillations 11. Measurability, integrability and absolute continuity 12. Boundedness and continuity via metric entropy 13. Integral representation 14. Historical notes and extensions
