Multivariate Control Charts for Monitoring the Process Mean and Variability Using Sequential Sampling

DOI10.1080/07474940701404898zbMath1116.62138OpenAlexW2043468777MaRDI QIDQ5297921

Marion R. jun. Reynolds, Keunpyo Kim

Publication date: 16 July 2007

Published in: Sequential Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/07474940701404898

zbMATH Keywords

tablesaverage time to signalstatistical process controlexponentially weighted moving average control chartmultivariate exponentially weighted moving average control chartregression adjustment of variablessquared deviations from targetvariable sampling ratesteady state average time to signal

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Sequential statistical analysis (62L10)

Related Items (10)

An adaptive variable-parameters scheme for the simultaneous monitoring of the mean and variability of an autocorrelated multivariate normal process ⋮ An adaptive max-type multivariate control chart by considering measurement errors and autocorrelation ⋮ Detecting changes in a multiparameter exponential family by using adaptive CUSUM procedure ⋮ Evaluation of multivariate surveillance ⋮ Accurate Tests and Intervals Based on Multivariate CUSUM Statistics ⋮ A Multivariate Adaptive Exponentially Weighted Moving Average Control Chart ⋮ Sufficient Reduction in Multivariate Surveillance ⋮ On Wald’s sequential test for vector mean under multivariate normal distribution and correlated data ⋮ Multivariate Control Charts for Monitoring the Mean Vector and Covariance Matrix with Variable Sampling Intervals ⋮ Optimal Sequential Surveillance for Finance, Public Health, and Other Areas

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