02858nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001600188072001700204072002300221072001500244072001600259100008500275245012900360250001800489264006900507300006700576336002600643337002600669338003600695347002400731505026600755520098301021650003302004650002602037650003302063650003202096710003402128773002602162776003602188776003602224776003602260856004602296912001402342912001502356999001702371978-3-031-57599-0DE-He21320251030112706.0cr nn 008mamaa240802s2024    sz |    s    |||| 0|eng d  a97830315759909978-3-031-57599-07 a10.1007/978-3-031-57599-02doi 4aQA274-274.9 7aPBT2bicssc 7aPBWL2bicssc 7aMAT0290402bisacsh 7aPBT2thema 7aPBWL2thema1 aResnick, Sidney.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut98178314aThe Art of Finding Hidden Risksh[electronic resource] :bHidden Regular Variation in the 21st Century /cby Sidney Resnick.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIII, 262 p. 105 illus., 73 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 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.  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. 0aStochastic processes.976770 0aProbabilities.96572414aStochastic Processes.97677024aApplied Probability.9657272 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303157598308iPrinted edition:z978303157600308iPrinted edition:z978303157601040uhttps://doi.org/10.1007/978-3-031-57599-0  aZDB-2-SMA  aZDB-2-SXMS  c62171d62171