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3622. Interarrival Distribution of a Periodically-Observed Time-Homogeneous Poisson Process
Invited abstract in session TD-39: Analysis of Stochastic Models I, stream Stochastic Modelling.
Tuesday, 14:30-16:00Room: 35 (building: 306)
Authors (first author is the speaker)
| 1. | Sheldon Jacobson
|
| Computer Science, University of Illinois | |
| 2. | Zach Hornberger
|
| University of Illinois | |
| 3. | Douglas King
|
| University of Illinois |
Abstract
Despite the prevalence of Poisson process modeling in social science, little research has explored how the periodic collection of data affects the applicability of this model. Our research studies how discretization impacts the Poisson process model’s interarrival distribution and introduces the periodically-observed time-homogeneous Poisson process (PTPP) model as a viable alternative. The long-run behavior of the PTPP is quantified and compared to the analogous Poisson process. We show the PTPP consistently provides a good approximation of process outputs regardless of discretization, extending the capabilities of the traditional model. The applicability of the PTPP model is demonstrated through multiple case studies.
Keywords
- Stochastic Models
- OR/MS and the Public Sector
Status: accepted
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