Embrechts, Paul, 1953-
Selfsimilar processes / Paul Embrechts and Makoto Maejima. - Princeton, N.J. : Princeton University Press, �2002. - 1 online resource (x, 111 pages) : illustrations - Princeton series in applied mathematics . - Princeton series in applied mathematics. .
Includes bibliographical references (pages 101-108) and index.
Contents; Preface; Chapter 1. Introduction; Chapter 2. Some Historical Background; Chapter 3. Selfsimilar Processes with Stationary Increments; Chapter 4. Fractional Brownian Motion; Chapter 5. Selfsimilar Processes with Independent Increments; Chapter 6. Sample Path Properties of Selfsimilar Stable Processes with Stationary Increments; Chapter 7. Simulation of Selfsimilar Processes; Chapter 8. Statistical Estimation; Chapter 9. Extensions; References; Index.
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 t.
In English.
1400814243 9781400814244 9781400825103 1400825105
10.1515/9781400825103 doi
22573/ctt10m2m JSTOR 9452497 IEEE
Self-similar processes.
Distribution (Probability theory)
Processus autosimilaires.
Distribution (Th�eorie des probabilit�es)
distribution (statistics-related concept)
MATHEMATICS--Probability & Statistics--Stochastic Processes.
Distribution (Probability theory)
Self-similar processes.
Electronic books.
Electronic books.
QA274.9 / .E43 2002eb
519.23
Selfsimilar processes / Paul Embrechts and Makoto Maejima. - Princeton, N.J. : Princeton University Press, �2002. - 1 online resource (x, 111 pages) : illustrations - Princeton series in applied mathematics . - Princeton series in applied mathematics. .
Includes bibliographical references (pages 101-108) and index.
Contents; Preface; Chapter 1. Introduction; Chapter 2. Some Historical Background; Chapter 3. Selfsimilar Processes with Stationary Increments; Chapter 4. Fractional Brownian Motion; Chapter 5. Selfsimilar Processes with Independent Increments; Chapter 6. Sample Path Properties of Selfsimilar Stable Processes with Stationary Increments; Chapter 7. Simulation of Selfsimilar Processes; Chapter 8. Statistical Estimation; Chapter 9. Extensions; References; Index.
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 t.
In English.
1400814243 9781400814244 9781400825103 1400825105
10.1515/9781400825103 doi
22573/ctt10m2m JSTOR 9452497 IEEE
Self-similar processes.
Distribution (Probability theory)
Processus autosimilaires.
Distribution (Th�eorie des probabilit�es)
distribution (statistics-related concept)
MATHEMATICS--Probability & Statistics--Stochastic Processes.
Distribution (Probability theory)
Self-similar processes.
Electronic books.
Electronic books.
QA274.9 / .E43 2002eb
519.23