| 000 | 03073nam a22005175i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-57599-0 | ||
| 003 | DE-He213 | ||
| 005 | 20251030112706.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240802s2024 sz | s |||| 0|eng d | ||
| 020 |
_a9783031575990 _9978-3-031-57599-0 |
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| 024 | 7 |
_a10.1007/978-3-031-57599-0 _2doi |
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| 050 | 4 | _aQA274-274.9 | |
| 072 | 7 |
_aPBT _2bicssc |
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_aPBWL _2bicssc |
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_aMAT029040 _2bisacsh |
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_aPBT _2thema |
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_aPBWL _2thema |
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| 100 | 1 |
_aResnick, Sidney. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981783 |
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| 245 | 1 | 4 |
_aThe Art of Finding Hidden Risks _h[electronic resource] : _bHidden Regular Variation in the 21st Century / _cby Sidney Resnick. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024. |
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| 300 |
_aXIII, 262 p. 105 illus., 73 illus. in color. _bonline resource. |
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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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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _a1 Foundation -- 2 Regular Variation -- 3 Hidden Regular Variation -- 4 Lévy Processes with Regularly Varying Distributions: Where Do the Jumps Go? -- 5 Statistics -- A A Crash Course on Regularly Varying Functions -- B Notation Summary -- References -- Index. | |
| 520 | _aThis text gives a comprehensive, largely self-contained treatment of multivariate heavy tail analysis. Emphasizing regular variation of measures means theory can be presented systematically and without regard to dimension. Tools are developed that allow a flexible definition of "extreme" in higher dimensions and permit different heavy tails to coexist on the same state space leading to "hidden regular variation" and "steroidal regular variation". This emphasizes when estimating risks, it is important to choose the appropriate heavy tail. Theoretical foundations lead naturally to statistical techniques; examples are drawn from risk estimation, finance, climatology and network analysis. Treatments target a broad audience in insurance, finance, data analysis, network science and probability modeling. The prerequisites are modest knowledge of analysis and familiarity with the definition of a measure; regular variation of functions is reviewed but is not a focal point. | ||
| 650 | 0 |
_aStochastic processes. _976770 |
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| 650 | 0 |
_aProbabilities. _965724 |
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| 650 | 1 | 4 |
_aStochastic Processes. _976770 |
| 650 | 2 | 4 |
_aApplied Probability. _965727 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031575983 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031576003 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031576010 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-57599-0 |
| 912 | _aZDB-2-SMA | ||
| 912 | _aZDB-2-SXMS | ||
| 999 |
_c62171 _d62171 |
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