Average run length comparison of multivariate control charts

Amir Javaheri · Ali A. Houshmand

16 citations0/0 files storedJournal of Statistical Computation and Simulation

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Advanced Statistical Process Monitoring Statistical Methods and Applications Fault Detection and Control Systems

Abstract

In this paper we use Monte Carlo Simulation methodology to compare the effectiveness of five multivariate quality control methods, namely Hotelling T 2, Multivariate Shewhart Char, Discriminant Analysis, Decomposition Method, and Multivariate Ridge Residual Chart-developed by Authors-, for controlling the mean vector in a multivariate process. P-dimensional multivariate normal data generated using different covariance structures. Various amount of shift in the mean vector is induced and the resulting Average Run Length (ARL) is computed. The effectiveness of each method with regard to ARL is discussed.

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References

2000Statistical Methods for Quality Improvement 1947Multivariate Quality Control 1991Multivariate Quality Control Based on Regression-Adiusted Variables 1995Decomposition of T 2 for Multivariate Control Chart Interpretation 1989Statistical Methods for Quality Improvement 1987Selecting Out of Control Variables With the T 2 Multivariate Quality Control Procedure

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2014Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control 2014An efficient multivariate control charting mechanism based on SPRT 2009Effect of Dependency in Systems for Multivariate Surveillance 2017Multivariate Statistical Quality Control Based on Ranked Set Sampling

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