Nonparametric analysis of univariate heavy-tailed data : research and practice / Natalia Markovich
Material type:
TextSeries: Publication details: hichester, England : John Wiley Sons, c2007Description: xxi, 310 p. : il. ; 24 cmISBN: - 0470510870
- 9780470510872
- QA 278 .8 M37
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Biblioteca Antonio Enriquez Savignac Estantería General (Primer piso) | Biblioteca Antonio Enriquez Savignac Estantería General (Primer piso) | COLECCIÓN RESERVA | QA 278 .8 M37 (Browse shelf(Opens below)) | Ejem. 1 | No para préstamo | Ingeniería en Datos e Inteligencia Organizacional | 031746 |
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| QA 76 .64 .672 R647 2004 Game architecture and design / | QA 76 .73 .P98 A525 2022 Python basics : a practical introduction to python 3 / | QA 76 .9 .I52 M167 1999 Database Access with Visual Basic 6 / | QA 278 .8 M37 Nonparametric analysis of univariate heavy-tailed data : research and practice / | QA 303 S66 1998 Suplemento del cálculo infinitesimal, calculus | QA 372 Z541 2009 Ecuaciones diferenciales con aplicaciones de modelado | QH 541 .15 .S64 E773 2022 Especies residentes de la Universidad del Caribe : grupo biodiversidad de la Universidad del Caribe / |
Incluye referencias bibliográficas: p. [295]-305 e índice
Definitions and rough detection of tail heaviness -- Classical methods of probability density estimation -- Heavy-tailed density estimation -- Transformations and heavy-tailed density estimation -- Classification and retransformed density estimates -- Estimation of high quantiles -- Nonparametric estimation of the hazard rate function -- Nonparametric estimation of the renewal function
"Heavy-tailed distributions are typical for phenomena in complex multi-component systems such as biometry, economics, ecological systems, sociology, web access statistics, internet traffic, biblio-metrics, finance and business. The analysis of such distributions requires special methods of estimation due to their specific features. These are not only the slow decay to zero of the tail, but also the violation of Cramer's condition, possible non-existence of some moments, and sparse observations in the tail of the distribution."--P. [4]
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