Monitoring Variability and Analyzing Multivariate Autocorrelated Processes

DOI10.1080/02664760701231849zbMath1157.62078OpenAlexW2038100178MaRDI QIDQ3604106

Jeffrey Jarrett, Xia Pan

Publication date: 24 February 2009

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664760701231849

zbMATH Keywords

VARSPCdiagnosing types of parameter shiftquality control for multivariate and serially correlated processesvariability shiftvector autoregressive residuals

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 (4)

A new proposal to solve the autocorrelation problem for monitoring processes in the cutlery industry ⋮ 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 ⋮ On the Run Length of a State-Space Control Chart for Multivariate Autocorrelated Data

Cites Work

• Monitoring the cross-covariances of a multivariate time series
• Multivariate Quality Control Chart for Autocorrelated Processes
• Applying State Space to SPC: Monitoring Multivariate Time Series
• Multivariate quality control
• The Multivariate Portmanteau Statistic
• A Multivariate Exponentially Weighted Moving Average Control Chart
• Run-Length Distributions of Special-Cause Control Charts for Correlated Processes
• On SPC for Short Run Autocorrelated Data
• Multivariate statistical process control—recent results and directions for future research

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