컨텐츠 시작
학술대회/행사
초록검색
| 제출번호(No.) | 0629 |
|---|---|
| 분류(Section) | Invited Talk |
| 분과(Session) | Probability / Stochastic Process / Statistics (SS-12) |
| 영문제목 (Title(Eng.)) |
Likelihood-based EWMA charts for monitoring Poisson count data with time-varying sample sizes |
| 저자(Author(s)) |
Qin Zhou1, Changliang Zou1, Zhaojun Wang1, Wei Jiang2 Nankai University1, Shanghai Jiaotong University2 |
| 초록본문(Abstract) | This article concerns with the problem of monitoring incidence rates of the Poisson distribution when sample size varies over time. Recently, a couple of cumulative sum and exponentially weighted moving average (EWMA) control charts have been proposed to deal with this problem by taking the varying sample size into consideration. However, we argue that some of these charts, which perform quite well in terms of average run length, may not be appealing in practice because they have rather unsatisfactory run length distributions. The probability of false alarms from these charts may increase dramatically after short-runs, which also results in extremely large standard deviation of run lengths. Motivated by the finding that the classical EWMA control chart can be derived under the framework of weighted likelihood, this paper suggests a new EWMA control chart which automatically integrates the varying sample sizes with the EWMA scheme. It is fast to compute, easy to construct and quite efficient in detecting changes of Poisson rates. Our simulation results show that the proposed chart is generally more effective and robust compared with existing EWMA charts. A health surveillance example based on mortality data from New Mexico is used to illustrate the implementation of the proposed method. |
| 분류기호 (MSC number(s)) |
70G45 |
| 키워드(Keyword(s)) | Poisson distribution, likelihood |
| 강연 형태 (Language of Session (Talk)) |
English |