eBook - ePub
Stable Non-Gaussian Random Processes
Stochastic Models with Infinite Variance
- 632 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
About this book
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.
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Yes, you can access Stable Non-Gaussian Random Processes by Gennady Samoradnitsky in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover Page
- Halftitle Page
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- Abbreviations
- Notation
- 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
- Appendix: A Tables of symmetric α-stable fractiles
- Bibliography
- Subject index
- Author index