Self-Starting Monitoring Scheme for Poisson Count Data With Varying Population Sizes

Abstract

In this article, we consider the problem of monitoring Poisson rates when the population sizes are time-varying and the nominal value of the process parameter is unavailable. Almost all previous control schemes for the detection of increases in the Poisson rate in Phase II are constructed based on assumed knowledge of the process parameters, for example, the expectation of the count of a rare event when the process of interest is in control. In practice, however, this parameter is usually unknown and not able to be estimated with a sufficiently large number of reference samples. A self-starting exponentially weighted moving average (EWMA) control scheme based on a parametric bootstrap method is proposed. The success of the proposed method lies in the use of probability control limits, which are determined based on the observations during rather than before monitoring. Simulation studies show that our proposed scheme has good in-control and out-of-control performance under various situations. In particular, our proposed scheme is useful in rare event studies during the start-up stage of a monitoring process. Supplementary materials for this article are available online.

• Average run length
• Healthcare surveillance
• Poisson process
• Probability control limits.

ACKNOWLEDGMENTS

The authors thank the editor, associate editor, and three anonymous referees for their many helpful comments that have resulted in significant improvements in the article. The research of Shen and Tsui was supported by RFCID 11101262, SRG 11212814, and the CRF CityU8/CRF/12G. The research of Woodall was supported by NSF Grant CMMI-1436365. Zou's research was supported by the NNSF of China Grants 11622104, 11431006, 11131002, 11371202, and the Foundation for the Author of National Excellent Doctoral Dissertation of PR China 201232. Zou is the corresponding author.