Bayesian change point estimation in Poisson-based control charts

Subject Areas : Mathematical Optimization

Hassan  Assareh ¹ (Simpson Centre for Health Services Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, NSW, 2052, Australia)
Rassoul  Noorossana ² (School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran)
Kerrie  L Mengersen ³ (Discipline of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, 4001, Australia)

Received: 2020-10-01 Accepted : 2020-10-01 Published : 2013-12-01

Keywords: Change point, control charts, Poisson Process, Bayesian hierarchical model, Markov chain Monte Carlo,

Abstract :

Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change, a linear trend and a known multiple number of changes in the Poisson rate. The Markov chain Monte Carlo is used to obtain posterior distributions of the change point parameters and corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the well-known c-, Poisson exponentially weighted moving average (EWMA) and Poisson cumulative sum (CUSUM) control charts for different change type scenarios. We also apply the Deviance Information Criterion as a model selection criterion in the Bayesian context, to find the best change point model for a given dataset where there is no prior knowledge about the change type in the process. In comparison with built-in estimators of EWMA and CUSUM charts and ML based estimators, the Bayesian estimator performs reasonably well and remains a strong alternative. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.