IEEE Technology Management Council Singapore Chapter
Likelihood-Based EWMA Charts for Monitoring Poisson Count Data with Time-Varying Sample Sizes
Prof Jiang Wei Antai College of Economics and Management, Shanghai Jiaotong University
15:00 p.m. to 16:00 p.m.
Faculty of Engineering, Seminar Room EA-06-02, NUS
This talk 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, we suggest 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.
Dr. Jiang Wei is the Professor and Head of the Department of Operations Management, Antai College of Economics and Management, Shanghai Jiaotong University. His research interests includes quality control and management, data mining and business analytics, logistics and operations management. He has published numerous papers in internationally recognized journals, and he is serving on the editorial board of several international journals.