Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
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E-Books | Biblioteca da FCTUNL Online | Não Ficção | QA273.6.SPR FCT 81241 (Browse shelf) | 1 | Available |
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QA273.6.SPR FCT 80791 Continuous bivariate distributions | QA273.6.SPR FCT 80935 Comparing distributions | QA273.6.SPR FCT 81028 Probability and statistical models | QA273.6.SPR FCT 81241 An introduction to heavy-tailed and subexponential distributions | QA273.6.SPR FCT 81353 Applications of discrete-time Markov chains and poisson processes to air pollution modeling and studies | QA273.6.SPR FCT 81453 Stochastic orders in reliability and risk | QA273.6.SPR FCT 81468 An introduction to heavy-tailed and subexponential distributions |
Colocação: Online
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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