It is well known that performance of control scheme in phase II of statistical process control depends on the estimators utilized in phase I. Sometimes, outliers may be present in the data, which could seriously impact the performance of the estimators. In some practical situations, generalized linear models (GLMs) are used to model a wide class of response variables. This study deals with the robust estimation and monitoring of parameters in GLM profiles in the presence of outliers. In this study, robust estimators are used to estimate the parameters of logistic and Poisson profiles. The results are compared with the maximum likelihood estimators (MLEs). In a numerical example, the profile parameters are estimated by the MLE and robust estimators and the resulting test statistics are monitored by a control scheme. The phase II control charts are determined based on these two types of estimators and compared for different out‐of‐control conditions. The simulation results confirm that robust estimators in most cases lead to better estimates in comparison with the MLE estimator in terms of average run length criterion.

中文翻译：

---

使用健壮的估算器进行GLM配置文件监控

众所周知，统计过程控制的第二阶段中控制方案的性能取决于第一阶段中使用的估计量。有时，数据中可能存在离群值，这可能会严重影响估计量的性能。在某些实际情况下，广义线性模型（GLM）用于对各种响应变量进行建模。这项研究处理了在存在异常值的情况下对GLM配置文件中参数的可靠估计和监视。在这项研究中，使用鲁棒估计器来估计逻辑和泊松分布图的参数。将结果与最大似然估计器（MLE）进行比较。在一个数值示例中，轮廓参数由MLE和鲁棒估计器估计，结果测试统计量由控制方案监控。根据这两种估算器确定II期控制图，并针对不同失控条件进行比较。仿真结果证实，就平均游程长度标准而言，与MLE估计器相比，鲁棒估计器在大多数情况下可带来更好的估计。