基于Bootstrap方法的控制图条件性能保证
Guaranteed Conditional Performance of Control Charts via Bootstrap Methods
摘要：
在实际应用中，通常需要估计控制图的受控状态。这种估计对控制图的性能有不利的影响，控制图的性能通常由例如虚警概率或平均游程长度来衡量。我们建议调整监测计划，以克服这些问题。它保证，以一定的概率，一个有条件的性能给定的估计在控制状态。建议的方法是基于自举的数据用于估计在控制状态。该方法适用于不同类型的控制图，也适用于基于回归模型，生存模型等的图表。如果使用非参数自助法，则该方法对模型误差具有鲁棒性。我们展示了调整的大样本性质。我们的方法的实用性是通过模拟研究证明。
英文摘要：
To use control charts in practice, the in-control state usually has to be estimated. This estimation has a detrimental effect on the performance of control charts, which is often measured for example by the false alarm probability or the average run length. We suggest an adjustment of the monitoring schemes to overcome these problems. It guarantees, with a certain probability, a conditional performance given the estimated in-control state. The suggested method is based on bootstrapping the data used to estimate the in-control state. The method applies to different types of control charts, and also works with charts based on regression models, survival models, etc. If a nonparametric bootstrap is used, the method is robust to model errors. We show large sample properties of the adjustment. The usefulness of our approach is demonstrated through simulation studies.
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