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Stable Non-Gaussian Self-Similar Processes with Stationary Increments
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Artikel-Nr:
9783319623313
Veröffentl:
2017
Einband:
eBook
Seiten:
135
Autor:
Vladas Pipiras
Serie:
SpringerBriefs in Probability and Mathematical Statistics
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:
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.
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.
Preliminaries.- Minimality, Rigidity, and Flows.- Mixed Moving Averages and Self-similarity.- A. Historical Notes.- B. Standard Lebesgue Spaces and Projections.- C. Notation Summary.