1970 Jan 01 (Thu), 08:00 AM
In many real applications of quality control schemes, there exist
significant measurement errors. For monitoring multivariate process
variability, we discover that the power of several control charts when the
quality characteristics contain measurement errors is greater than that when
the quality characteristics do not contain measurement errors. Because quality
characteristics without measurement errors are more informative than those with
measurement errors, this phenomenon is unreasonable. In this talk, we
investigate this phenomenon on many commonly used control charts for monitoring
multivariate process variability, including those derived based on sample
generalized variance, sum of the standardized variance for the principal
component, regression-adjusted variables, and modified likelihood ratio test.
An adequate control chart for monitoring multivariate process variability when
there exist measurement errors will be proposed.
This is a joint work with Ying Hung.