Compound Markov counting processes and their applications to modeling infinitesimally over-dispersed systems
We propose an infinitesimal dispersion index for Markov counting processes. We show that, under standard moment existence conditions, a process is infinitesimally (over-)equi-dispersed if, and only if, it is simple (compound), i.e.Â it increases in jumps of one (or more) unit(s), even though infinitesimally equi-dispersed processes might be under-, equi- or over-dispersed using previously studied indices. Compound processes arise, for example, when introducing continuous-time white noise to the rates of simple processes resulting in Lévy-driven SDEs. We construct multivariate infinitesimally over-dispersed compartment models and queuing networks, suitable for applications where moment constraints inherent to simple processes do not hold.
Volume (Year): 121 (2011)
Issue (Month): 11 (November)
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- Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342.
- Fan, Ruzong & Lange, Kenneth & Peña, Edsel, 1999. "Applications of a formula for the variance function of a stochastic process," Statistics & Probability Letters, Elsevier, vol. 43(2), pages 123-130, June.
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