About this book
This book provides a self-contained presentation on the structure of a large class of stable processes, known as self-similar mixed moving averages. The authors present a way to describe and classify these processes by relating them to so-called deterministic flows. The first sections in the book review random variables, stochastic processes, and integrals, moving on to rigidity and flows, and finally ending with mixed moving averages and self-similarity. In-depth appendices are also included.
This book is aimed at graduate students and researchers working in probability theory and statistics.
- Book Title Stable Non-Gaussian Self-Similar Processes with Stationary Increments
- Series Title SpringerBriefs in Probability and Mathematical Statistics
- Series Abbreviated Title SpringerBriefs in Probabil., Math.Statist.
- DOI https://doi.org/10.1007/978-3-319-62331-3
- Copyright Information The Author(s) 2017
- Publisher Name Springer, Cham
- eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
- Softcover ISBN 978-3-319-62330-6
- eBook ISBN 978-3-319-62331-3
- Series ISSN 2365-4333
- Series E-ISSN 2365-4341
- Edition Number 1
- Number of Pages XIII, 135
- Number of Illustrations 2 b/w illustrations, 0 illustrations in colour
Probability Theory and Stochastic Processes
Dynamical Systems and Ergodic Theory
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