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Paul Embrechts - Selfsimilar Processes - 9780691096278 - V9780691096278
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Selfsimilar Processes

€ 95.05
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Description for Selfsimilar Processes Hardback. The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. With an historical overview, this book describes the state of knowledge about selfsimilar processes and their applications. It emphasizes concepts, definitions and basic properties, giving the reader a road map of the realm of selfsimilarity. Series: Princeton Series in Applied Mathematics. Num Pages: 128 pages, 1, black & white illustrations. BIC Classification: PBWL. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 237 x 162 x 15. Weight in Grams: 350.
The modeling of stochastic dependence is fundamental for understanding random systems evolving in time. When measured through linear correlation, many of these systems exhibit a slow correlation decay--a phenomenon often referred to as long-memory or long-range dependence. An example of this is the absolute returns of equity data in finance. Selfsimilar stochastic processes (particularly fractional Brownian motion) have long been postulated as a means to model this behavior, and the concept of selfsimilarity for a stochastic process is now proving to be extraordinarily useful. Selfsimilarity translates into the equality in distribution between the process under a linear time change and the same process properly scaled in space, a simple scaling property that yields a remarkably rich theory with far-flung applications. After a short historical overview, this book describes the current state of knowledge about selfsimilar processes and their applications. Concepts, definitions and basic properties are emphasized, giving the reader a road map of the realm of selfsimilarity that allows for further exploration. Such topics as noncentral limit theory, long-range dependence, and operator selfsimilarity are covered alongside statistical estimation, simulation, sample path properties, and stochastic differential equations driven by selfsimilar processes. Numerous references point the reader to current applications. Though the text uses the mathematical language of the theory of stochastic processes, researchers and end-users from such diverse fields as mathematics, physics, biology, telecommunications, finance, econometrics, and environmental science will find it an ideal entry point for studying the already extensive theory and applications of selfsimilarity.

Product Details

Format
Hardback
Publication date
2002
Publisher
Princeton University Press United States
Number of pages
128
Condition
New
Series
Princeton Series in Applied Mathematics
Number of Pages
128
Place of Publication
New Jersey, United States
ISBN
9780691096278
SKU
V9780691096278
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

About Paul Embrechts
Paul Embrechts is Professor of Mathematics at the Swiss Federal Institute of Technology (ETHZ), Zurich, Switzerland. He is the author of numerous scientific papers on stochastic processes and their applications and the coauthor of the influential book on "Modelling of Extremal Events for Insurance and Finance". Makoto Maejima is Professor of Mathematics at Keio University, Yokohama, Japan. He has published extensively on selfsimilarity and stable processes.

Reviews for Selfsimilar Processes
"Authoritative and written by leading experts, this book is a significant contribution to a growing field. Selfsimilar processes crop up in a wide range of subjects from finance to physics, so this book will have a correspondingly wide readership."—Chris Rogers, Bath University "This is a timely book. Everybody is talking about scaling, and selfsimilar stochastic processes are the basic and the clearest examples of models with scaling. In applications from finance to communication networks, selfsimilar processes are believed to be important. Yet much of what is known about them is folklore; this book fills the void and gives reader access to some hard facts. And because this book requires only modest mathematical sophistication, it is accessible to a wide audience."—Gennady Samorodnitsky, Cornell University

Goodreads reviews for Selfsimilar Processes


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