الفهرس 
0 Phase II monitoring of zero inflated poisson regression profiles
Abstract EnglishOnly 14 pages are availabe for public view
 |  | from | 128 |  |  |
| | | from | 128 | | |
Abstract
In many quality control applications, the quality of some products or processes is best characterized by a functional relationship (profile) between a response variable and one or more explanatory variables. Profile monitoring involves the use of control charts to monitor the stability of this type of quality control processes. Several studies have discussed the problem of monitoring normal response profiles. More recently, researchers started studying the case where the response variable follows a discrete distribution such as the Poisson or the Bernoulli distributions. Due to recent technological advancement used in almost all manufacturing processes, there exist near-zero defect manufacturing processes, hence, the zero-inflated Poisson distribution is expected to be more appropriate than the usual Poisson distribution for monitoring such processes. This study aims at extending three of phase II profile monitoring approaches, namely MEWMA, Hotelling{u2019}s T2, and EWMA-R to the case of zero-inflated Poisson profiles. A Simulation study is used to compare the performance of these approaches in terms of the average run length (ARL) and the standard deviation run length (SDRL). The results revealed that, when considering a profile with one covariate, the EWMA-R chart is superior to the other two methods. In addition, increasing the profile size improves the performance of the suggested methods significantly. However, in the case of profiles with 3 or 5 covariates, the MEWMA chart and the EWMA-R chart show similar performance which is better than that of the T2 method in detecting small shifts. Finally, the proposed approaches were applied on a real dataset to illustrate the performance of the suggested approaches