Atnaujinkite slapukų nuostatas

El. knyga: Art of Finding Hidden Risks: Hidden Regular Variation in the 21st Century

Kitos knygos pagal šią temą:
Art of Finding Hidden Risks: Hidden Regular Variation in the 21st CenturyDidesnis paveikslėlis
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

This 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.



1 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.
Sidney Resnick is the Lee Teng-Hui Professor in Engineering Emeritus in Cornell University's School of Operations Research and Information Engineering in Ithaca NY. He joined Cornell after posts at Technion, Stanford and Colorado State University. He has served on numerous editorial boards, had numerous visiting appointments and, to date, has published 4 previous books and co-authored 195 research papers. From 1998--2003, Resnick was Director of the School of ORIE.
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97830315759902e.html