Selfsimilar processes / Paul Embrechts and Makoto Maejima.
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...
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| Format: | eBook |
| Language: | English |
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Princeton, N.J. :
Princeton University Press,
©2002.
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| Series: | Princeton series in applied mathematics.
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| Subjects: |
MARC
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| 100 | 1 | |a Embrechts, Paul, |d 1953- | |
| 245 | 1 | 0 | |a Selfsimilar processes / |c Paul Embrechts and Makoto Maejima. |
| 260 | |a Princeton, N.J. : |b Princeton University Press, |c ©2002. | ||
| 300 | |a 1 online resource (x, 111 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent. | ||
| 337 | |a computer |b c |2 rdamedia. | ||
| 338 | |a online resource |b cr |2 rdacarrier. | ||
| 490 | 1 | |a Princeton series in applied mathematics. | |
| 504 | |a Includes bibliographical references (pages 101-108) and index. | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 546 | |a In English. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Self-similar processes. | |
| 650 | 0 | |a Distribution (Probability theory) | |
| 650 | 7 | |a Distribution (Probability theory) |2 fast |0 (OCoLC)fst00895600. | |
| 650 | 7 | |a Self-similar processes. |2 fast |0 (OCoLC)fst01111938. | |
| 700 | 1 | |a Maejima, Makoto. | |
| 776 | 0 | 8 | |i Print version: |a Embrechts, Paul, 1953- |t Selfsimilar processes. |d Princeton, N.J. : Princeton University Press, ©2002 |z 0691096279 |w (DLC) 2002108314 |w (OCoLC)48837321. |
| 830 | 0 | |a Princeton series in applied mathematics. | |
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