Título: Sample Path Large Deviations Principles for Poisson Shot Noise Processes and Applications
Autores: Ganesh, Ayalvadi; Microsoft Research
Macci, Claudio; Universita degli Studi di Roma
Torrisi, Giovanni Luca; Istituto per le Applicazioni del Calcolo
Fecha: 2005-01-01
Publicador: Electronic journal of probability
Fuente:
Tipo: Peer-reviewed Article

Tema: Poisson shot noise; large deviations; sample paths; queues; risk
60F10, 60F17,60K25
Descripción: This paper concerns sample path large deviations for Poisson shot noise processes, and applications in queueing theory. We first show that, under an exponential tail condition, Poisson shot noise processes satisfy a sample path large deviations principle with respect to the topology of pointwise convergence. Under a stronger superexponential tail condition, we extend this result to the topology of uniform convergence. We also give applications of this result to determining the most likely path to overflow in a single server queue, and to finding tail asymptotics for the queue lengths at priority queues.
Idioma: No aplica

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