Check nearby libraries
Buy this book
The familiar Gaussian models do not allow for large deviations and are thus often inadequate for modeling high variability. Non-Gaussian stable models do not possess such limitations. They all share a familiar feature which differentiates them from the Gaussian ones. Their marginal distributions possess heavy "probability tails," always with infinite variance and in some cases with infinite first moment.
The aim of this book is to make this exciting material easily accessible to graduate students and practitioners. Assuming only a first-year graduate course in probability, it includes material which has appeared only recently in journals and unpublished materials.
Each chapter begins with a brief overview and concludes with a range of exercises at varying levels of difficulty. Proofs are spelled out in detail. The book includes a discussion of self-similar processes, ARMA, and fractional ARIMA time series with stable innovations.
Check nearby libraries
Buy this book
Previews available in: English
Showing 1 featured edition. View all 1 editions?
Edition | Availability |
---|---|
1
Stable non-Gaussian random processes: stochastic models with infinite variance
1994, Chapman & Hall
in English
0412051710 9780412051715
|
aaaa
Libraries near you:
WorldCat
|
Book Details
Edition Notes
Includes bibliographical references (p. [603]-619) and indexes.
Classifications
The Physical Object
ID Numbers
Community Reviews (0)
Feedback?History
- Created April 1, 2008
- 12 revisions
Wikipedia citation
×CloseCopy and paste this code into your Wikipedia page. Need help?
July 25, 2024 | Edited by MARC Bot | import existing book |
December 4, 2022 | Edited by ImportBot | import existing book |
November 5, 2022 | Edited by ImportBot | import existing book |
September 15, 2021 | Edited by ImportBot | import existing book |
April 1, 2008 | Created by an anonymous user | Imported from Scriblio MARC record |