000 | 05219cam a2200913Ia 4500 | ||
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001 | ocm52255009 | ||
003 | OCoLC | ||
005 | 20220908095959.0 | ||
006 | m o d | ||
007 | cr cn||||||||| | ||
008 | 030515s2002 njua ob 001 0 eng d | ||
010 | _z 2002108314 | ||
040 |
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_a1400814243 _q(electronic bk.) |
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_a9781400814244 _q(electronic bk.) |
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_a9781400825103 _q(electronic bk.) |
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_a1400825105 _q(electronic bk.) |
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_z0691096279 _q(alk. paper) |
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024 | 7 |
_a10.1515/9781400825103 _2doi |
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037 |
_a22573/ctt10m2m _bJSTOR |
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037 |
_a9452497 _bIEEE |
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050 | 4 |
_aQA274.9 _b.E43 2002eb |
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072 | 7 |
_aMAT _x029040 _2bisacsh |
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082 | 0 | 4 |
_a519.23 _221 |
084 |
_aSK 820 _2rvk |
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049 | _aMAIN | ||
100 | 1 |
_aEmbrechts, Paul, _d1953- _963664 |
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245 | 1 | 0 |
_aSelfsimilar processes / _cPaul Embrechts and Makoto Maejima. |
260 |
_aPrinceton, N.J. : _bPrinceton University Press, _c�2002. |
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300 |
_a1 online resource (x, 111 pages) : _billustrations |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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490 | 1 | _aPrinceton series in applied mathematics | |
504 | _aIncludes bibliographical references (pages 101-108) and index. | ||
588 | 0 | _aPrint version record. | |
505 | 0 | _aContents; 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 | _aThe 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 | _aIn English. | ||
590 |
_aIEEE _bIEEE Xplore Princeton University Press eBooks Library |
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650 | 0 |
_aSelf-similar processes. _930990 |
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650 | 0 |
_aDistribution (Probability theory) _910767 |
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650 | 6 |
_aProcessus autosimilaires. _963665 |
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650 | 6 |
_aDistribution (Th�eorie des probabilit�es) _963666 |
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650 | 7 |
_adistribution (statistics-related concept) _2aat _963667 |
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650 | 7 |
_aMATHEMATICS _xProbability & Statistics _xStochastic Processes. _2bisacsh _963668 |
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650 | 7 |
_aDistribution (Probability theory) _2fast _0(OCoLC)fst00895600 _910767 |
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650 | 7 |
_aSelf-similar processes. _2fast _0(OCoLC)fst01111938 _930990 |
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655 | 0 |
_aElectronic books. _93294 |
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655 | 4 |
_aElectronic books. _93294 |
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700 | 1 |
_aMaejima, Makoto. _963669 |
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776 | 0 | 8 |
_iPrint version: _aEmbrechts, Paul, 1953- _tSelfsimilar processes. _dPrinceton, N.J. : Princeton University Press, �2002 _z0691096279 _w(DLC) 2002108314 _w(OCoLC)48837321 |
830 | 0 |
_aPrinceton series in applied mathematics. _963670 |
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856 | 4 | 0 | _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=9452497 |
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