Exponential ergodicity and steady-state approximations for a class of markov processes under fast regime switching

Ari Arapostathis, Guodong Pang, Yi Zheng

Research output: Contribution to journalArticlepeer-review

Abstract

We study ergodic properties of a class of Markov-modulated general birth-death processes under fast regime switching. The first set of results concerns the ergodic properties of the properly scaled joint Markov process with a parameter that is taken to be large. Under very weak hypotheses, we show that if the averaged process is exponentially ergodic for large values of the parameter, then the same applies to the original joint Markov process. The second set of results concerns steady-state diffusion approximations, under the assumption that the 'averaged' fluid limit exists. Here, we establish convergence rates for the moments of the approximating diffusion process to those of the Markov-modulated birth-death process. This is accomplished by comparing the generator of the approximating diffusion and that of the joint Markov process. We also provide several examples which demonstrate how the theory can be applied.

Original languageEnglish (US)
Pages (from-to)1-29
Number of pages29
JournalAdvances in Applied Probability
Volume53
Issue number1
DOIs
StatePublished - Mar 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Applied Mathematics

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