Maximum likelihood estimation in the two-state Markovian arrival process
Emilio Carrizosa, Pepa Ram\'irez-Cobo
TL;DR
This paper investigates maximum likelihood estimation methods for the two-state Markovian arrival process, a model used in various fields, by leveraging a recent parameterization to improve estimation accuracy.
Contribution
It introduces a new approach to estimate parameters of the two-state MAP using maximum likelihood with a novel parameterization.
Findings
Effective estimation of two-state MAP parameters demonstrated
Improved understanding of MAP modeling in queueing and teletraffic
Potential for enhanced applications in finance and reliability
Abstract
The Markovian arrival process (MAP) has proven a versatile model for fitting dependent and non-exponential interarrival times, with a number of applications to queueing, teletraffic, reliability or finance. Despite theoretical properties of MAPs and models involving MAPs are well studied, their estimation remains less explored. This paper examines maximum likelihood estimation of the second-order MAP using a recently obtained parameterization of the two-state MAPs.
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Taxonomy
TopicsAdvanced Queuing Theory Analysis · Reliability and Maintenance Optimization · Probability and Risk Models