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Selfsimilar processes / Paul Embrechts and Makoto Maejima.

By: Embrechts, Paul, 1953-.
Contributor(s): Maejima, Makoto.
Material type: materialTypeLabelBookSeries: Princeton series in applied mathematics: Publisher: Princeton, N.J. : Princeton University Press, �2002Description: 1 online resource (x, 111 pages) : illustrations.Content type: text Media type: computer Carrier type: online resourceISBN: 1400814243; 9781400814244; 9781400825103; 1400825105.Subject(s): 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 processesGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: Selfsimilar processes.DDC classification: 519.23 Other classification: SK 820 Online resources: Click here to access online
Contents:
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.
Summary: 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.
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Includes bibliographical references (pages 101-108) and index.

Print version record.

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.

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