Abbreviated Journal Title
Qual. Technol. Quant. Manag.
Keywords
Average run length (ARL); covariance matrix; multi standardization; penalized likelihood function; MULTIVARIATE PROCESS VARIABILITY; SUM CONTROL CHARTS; INDIVIDUAL; OBSERVATIONS; LIKELIHOOD-ESTIMATION; PENALIZED LIKELIHOOD; SAMPLE; VARIANCES; SELECTION; MODEL; Engineering, Industrial; Operations Research & Management Science; Statistics & Probability
Abstract
Multivariate control charts are essential tools in multivariate statistical process control. In real applications, when a multivariate process shifts, it occurs in either location or scale. Several methods have been proposed recently to monitor the covariance matrix. Most of these methods use rational subgroups and are used to detect large shifts. In this paper, we propose a new accumulative method, based on penalized likelihood estimators, that uses individual observations and is useful to detect small and persistent shifts in a process when sparsity is present.
Journal Title
Quality Technology and Quantitative Management
Volume
10
Issue/Number
1
Publication Date
1-1-2013
Document Type
Article
Language
English
First Page
95
Last Page
114
WOS Identifier
ISSN
1684-3703
Recommended Citation
Maboudou-Tchao, Edward M. and Diawara, Norou, "A LASSO Chart for Monitoring the Covariance Matrix" (2013). Faculty Bibliography 2010s. 4357.
https://stars.library.ucf.edu/facultybib2010/4357
Comments
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