Selfsimilar Processes
jan. 2009 · Princeton University Press
E-bok
128
Sidor
family_home
Kvalificerad
info
reportBetyg och recensioner verifieras inte Läs mer
Om den här e-boken
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.
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.
Om författaren
Paul Embrechts is Professor of Mathematics at the Swiss Federal Institute of Technology (ETHZ), Zürich, 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.
Betygsätt e-boken
Berätta vad du tycker.
Läsinformation
Smartphones och surfplattor
Installera appen Google Play Böcker för Android och iPad/iPhone. Appen synkroniseras automatiskt med ditt konto så att du kan läsa online eller offline var du än befinner dig.
Laptops och stationära datorer
Du kan lyssna på ljudböcker som du har köpt på Google Play via webbläsaren på datorn.
Läsplattor och andra enheter
Om du vill läsa boken på enheter med e-bläck, till exempel Kobo-läsplattor, måste du ladda ned en fil och överföra den till enheten. Följ anvisningarna i hjälpcentret om du vill överföra filerna till en kompatibel läsplatta.
