EWMA charts for monitoring the mean and the autocovariances of stationary processes
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Publication:849882
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
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.
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- 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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