EWMA charts for monitoring the mean and the autocovariances of stationary processes

DOI10.1007/s00362-006-0308-9zbMath1125.62133OpenAlexW2033308885MaRDI QIDQ849882

Wolfgang Schmid, M. Rosołowski

Publication date: 14 November 2006

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-006-0308-9

zbMATH Keywords

time series analysisstatistical process controlsimultaneous control charts

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (3)

Control charts for measurement error models ⋮ Monitoring mean changes in persistent multivariate time series ⋮ The \(\operatorname{ARIMA}(p,d,q)\) on upper sided of CUSUM procedure

Cites Work

• Time series: theory and methods.
• Consistent autoregressive spectral estimates
• CUSUM control schemes for Gaussian processes
• The influence of parameter estimation on the ARL of Shewhart type charts for time series
• Control charts for GARCH processes.
• On the run length of a Shewhart chart for correlated data
• Gaussian and non-Gaussian linear time series and random fields
• Joint Monitoring of Process Mean and Variance Using Exponentially Weighted Moving Average Control Charts
• A Multivariate Exponentially Weighted Moving Average Control Chart
• Performance of CUSUM Control Schemes for Serially Correlated Observations
• Run-Length Distributions of Special-Cause Control Charts for Correlated Processes
• SEQUENTIAL METHODS FOR DETECTING CHANGES IN THE VARIANCE OF ECONOMIC TIME SERIES
• Ewma charts for multivariate time series
• EWMA Charts for Monitoring the Mean and the Autocovariances of Stationary Gaussian Processes
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