Multivariate quality control based on regression-adjusted variables
Multivariate quality control based on regression-adjusted variables
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DOI：
10.2307/1269008
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发表时间：
1991-02
影响因子：
2
通讯作者：
D. Hawkins
中科院分区：
文献类型：
--
作者：
D. Hawkins
当在由几个可能相关的变量进行测量的情况下执行质量控制时，最好使用利用变量之间的关系的方法来提供比单独对变量进行的控制更敏感的控制。将变量向量作为一个整体进行评估的最常见的多变量质量控制方法是基于变量与规格向量之间的霍特林T2的方法。虽然对于均值向量中的一般多变量漂移，T2是最优单检验统计量，但对于更结构化的均值漂移--例如，仅在某些变量中的漂移--它并不是最优的。基于二次型(如T2)的度量也混淆了均值漂移和方差漂移，并且需要在信号之后进行相当广泛的分析来确定漂移的性质。本文提出了基于标度残差向量Z的休哈特和累积和(CUSUM)控制。
When performing quality control in a situation in which measures are made of several possibly related variables, it is desirable to use methods that capitalize on the relationship between the variables to provide controls more sensitive than those that may be made on the variables individually. The most common methods of multivariate quality control that assess the vector of variables as a whole are those based on the Hotelling T 2 between the variables and the specification vector. Although T 2 is the optimal single-test statistic for a general multivariate shift in the mean vector, it is not optimal for more structured mean shifts-for example, shifts in only some of the variables. Measures based on quadratic forms (like T 2) also confound mean shifts with variance shifts and require quite extensive analysis following a signal to determine the nature of the shift. This article proposes Shewhart and cumulative sum (CUSUM) controls based on the vector Z of scaled residuals from the regression of each varia...