Abstract

A robust multivariate quality control technique for individual observations is proposed, based on the robust reweighted shrinkage estimators. A simulation study is done to check the performance and compare the method with the classical Hotelling approach, and the robust alternative based on the reweighted minimum covariance determinant estimator. The results show the appropriateness of the method even when the dimension or the Phase I contamination are high, with both independent and correlated variables, showing additional advantages about computational efficiency. The approach is illustrated with two real data-set examples from production processes.

摘要

基于稳健的重新加权收缩估计量，提出了一种用于个体观测的稳健多变量质量控制技术。进行了模拟研究以检查性能并将该方法与经典的 Hotelling 方法以及基于重新加权最小协方差行列式估计器的稳健替代方法进行比较。结果表明，即使在维度或第一阶段污染很高时，该方法的适用性也是如此，同时具有独立变量和相关变量，显示出计算效率方面的额外优势。该方法通过来自生产过程的两个真实数据集示例进行了说明。