Surveillance strategies for detecting changepoint in incidence rate based on exponentially weighted moving average methods

• 成果类型: Article
• 署名作者: Dong, Yuping; Hedayat, A. S.; Sinha, B. K.
• 署名单位: Bristol-Myers Squibb; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Indian Statistical Institute; Indian Statistical Institute Kolkata
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000166
• 发表日期: 2008
• 页码: 843-853
• 关键词: ewma control charts; run-length; Cusum

摘要

Surveillance is a major issue in the today's world. The need to develop adequate surveillance strategies is genuine in many spheres of human activity. In this article we focus on a specific problem of surveillance and discuss some related statistical issues. This specific problem deals with a possible change in the incidence rate of an event (to a higher value) when a system is studied at discrete time points. We apply the exponentially weighted moving average methods for detecting an increased incidence rate per exposure unit of an event. Different measures of evaluation, suitable in different types of applications, such as the expected delay, out-of-control average run length, and probability of successful detection, are studied. Analytical bounds are provided for those measures of evaluation. Results from intensive simulations indicate that the analytical bounds perform fairly well when the weight parameter is large.