Monitoring the covariance matrix with fewer observations than variables

DOI10.1016/j.csda.2013.02.028zbMath1468.62129OpenAlexW2059290350MaRDI QIDQ1800078

Edgard M. Maboudou-Tchao, Vincent Agboto

Publication date: 19 October 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.2013.02.028

zbMATH Keywords

covariance matrixCholesky decompositionaverage run length (ARL)multistandardizationpenalized likelihood function

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 (5)

Covariance structure regularization via entropy loss function ⋮ Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation ⋮ Directional monitoring and diagnosis for covariance matrices ⋮ High-dimensional data monitoring using support machines ⋮ Kernel methods for changes detection in covariance matrices

Uses Software

• glasso

Cites Work

• Unnamed Item
• Sparse inverse covariance estimation with the graphical lasso
• Sparsistency and rates of convergence in large covariance matrix estimation
• Testing variability in multivariate quality control: a conditional entropy measure approach
• Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation
• Nonconcave penalized likelihood with a diverging number of parameters.
• Least angle regression. (With discussion)
• Sparse permutation invariant covariance estimation
• Model selection and estimation in the Gaussian graphical model
• First-Order Methods for Sparse Covariance Selection
• The comparison of sample covariance matrices using likelihood ratio tests
• Determinant Maximization with Linear Matrix Inequality Constraints

This page was built for publication: Monitoring the covariance matrix with fewer observations than variables