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Inference for double Pareto lognormal queues with applications

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  • Pepa Ramirez

    ()

  • Rosa E. Lillo

    ()

  • Michael P. Wiper

    ()

  • Simon P. Wilson

    ()

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    Abstract

    In this article we describe a method for carrying out Bayesian inference for the double Pareto lognormal (dPlN) distribution which has recently been proposed as a model for heavy-tailed phenomena. We apply our approach to inference for the dPlN/M/1 and M/dPlN/1 queueing systems. These systems cannot be analyzed using standard techniques due to the fact that the dPlN distribution does not posses a Laplace transform in closed form. This difficulty is overcome using some recent approximations for the Laplace transform for the Pareto/M/1 system. Our procedure is illustrated with applications in internet traffic analysis and risk theory.

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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws080402.

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    Date of creation: Feb 2008
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    Handle: RePEc:cte:wsrepe:ws080402

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    Related research

    Keywords: Heavy tails; Bayesian inference; Queueing theory;

    This paper has been announced in the following NEP Reports:

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