An Introduction to Heavy-Tailed and Subexponential Distributions [recurso electrónico] / by Sergey Foss, Dmitry Korshunov, Stan Zachary.

Por: Foss, Sergey [author.]Colaborador(es): Korshunov, Dmitry [author.] | Zachary, Stan [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Springer Series in Operations Research and Financial Engineering ; 38Editor: New York, NY : Springer New York, 2011Edición: 1Descripción: IX, 123p. 1 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781441994738Tema(s): Mathematics | Distribution (Probability theory) | Economics -- Statistics | Mathematics | Probability Theory and Stochastic Processes | Statistics for Business/Economics/Mathematical Finance/Insurance | Statistical Physics, Dynamical Systems and ComplexityFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 519.2 Clasificación LoC:QA273.A1-274.9QA274-274.9Recursos en línea: Libro electrónicoTexto
Contenidos:
Preface -- Introduction -- Heavy- and long-tailed distributions -- Subexponential distributions.- Densities and local probabilities -- Maximum of random walks -- References -- Index.
En: Springer eBooksResumen: Heavy-tailed probability distributions are an important component in the modeling of many stochastic systems. They are frequently used to accurately model inputs and outputs of computer and data networks and service facilities such as call centers. They are an essential for describing risk processes in finance and also for insurance premia pricing, and such distributions occur naturally in models of epidemiological spread. The class includes distributions with power law tails such as the Pareto, as well as the lognormal and certain Weibull distributions.  This monograph defines the classes of long-tailed and subexponential distributions in one dimension and provides a complete and comprehensive description of their properties. New results are presented in a simple, coherent and systematic way. This leads to a comprehensive exposition of tail properties of sums of independent random variables whose distributions belong to the long-tailed and subexponential class. The book includes a discussion of and references to contemporary areas of applications and also contains preliminary mathematical material which makes the book self contained. Modelers in the fields of finance, insurance, network science and environmental studies will find this book to be an essential reference.
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Colección de Libros Electrónicos QA273 .A1-274.9 (Browse shelf(Abre debajo)) 1 No para préstamo 372234-2001

Preface -- Introduction -- Heavy- and long-tailed distributions -- Subexponential distributions.- Densities and local probabilities -- Maximum of random walks -- References -- Index.

Heavy-tailed probability distributions are an important component in the modeling of many stochastic systems. They are frequently used to accurately model inputs and outputs of computer and data networks and service facilities such as call centers. They are an essential for describing risk processes in finance and also for insurance premia pricing, and such distributions occur naturally in models of epidemiological spread. The class includes distributions with power law tails such as the Pareto, as well as the lognormal and certain Weibull distributions.  This monograph defines the classes of long-tailed and subexponential distributions in one dimension and provides a complete and comprehensive description of their properties. New results are presented in a simple, coherent and systematic way. This leads to a comprehensive exposition of tail properties of sums of independent random variables whose distributions belong to the long-tailed and subexponential class. The book includes a discussion of and references to contemporary areas of applications and also contains preliminary mathematical material which makes the book self contained. Modelers in the fields of finance, insurance, network science and environmental studies will find this book to be an essential reference.

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