Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation

DOI10.1016/j.csda.2014.04.017zbMath1506.62186OpenAlexW2048500077MaRDI QIDQ1623641

Kaibo Wang, Arthur B. Yeh, Bo Li

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.04.017

zbMATH Keywords

likelihood ratio testpenalized likelihood estimation\(L_1\) penalty functionphase II monitoring

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (10)

Directional monitoring and diagnosis for covariance matrices ⋮ High-dimensional data monitoring using support machines ⋮ Kernel methods for changes detection in covariance matrices ⋮ Multivariate ELR control chart with estimated mean vector and covariance matrix ⋮ <scp>Location‐scale</scp> monitoring of ordinal categorical processes ⋮ 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 ⋮ Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring ⋮ Monitoring the covariance matrix with fewer observations than variables ⋮ Comparison of joint control schemes for multivariate normal i.i.d. output

Uses Software

• spcov

Cites Work

• Unnamed Item
• Sparse estimation of a covariance matrix
• Statistics for high-dimensional data. Methods, theory and applications.
• Monitoring the covariance matrix with fewer observations than variables
• A control chart based on likelihood ratio test for detecting patterned mean and variance shifts
• Multivariate Statistical Process Control Using LASSO
• Self-starting control chart for simultaneously monitoring process mean and variance
• Extended Bayesian information criteria for model selection with large model spaces
• An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative

This page was built for publication: Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation