An adaptive exponentially weighted moving average control chart for monitoring process variances

DOI10.1080/00949650601108000zbMath1136.62090OpenAlexW2029913468MaRDI QIDQ3497827

Lianjie Shu

Publication date: 28 May 2008

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949650601108000

zbMATH Keywords

Markov chainstatistical process controlaverage run lengthnormalizing transformationdispersion charts

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Related Items (8)

On the performance of the adaptive EWMA chart for monitoring time between events ⋮ New weighted adaptive CUSUM charts for monitoring the generalized variance of a bivariate normal process ⋮ The adaptive EWMA median chart for known and estimated parameters ⋮ Development of a multiattribute synthetic-np chart ⋮ Monitoring simple linear profiles using variable sample size schemes ⋮ Adaptive multivariate EWMA charts for monitoring sparse mean shifts based on parameter optimization design ⋮ An adaptive exponentially weighted moving average-type control chart to monitor the process mean ⋮ Enhanced operation of wastewater treatment plant using state estimation-based fault detection strategies

Cites Work

• Optimal stopping times for detecting changes in distributions
• Robust regression using iteratively reweighted least-squares
• The Distribution of Chi-Square
• An approach to the probability distribution of cusum run length
• Robust Statistics

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