Choosing principal components for multivariate statistical process control

George C. Runger

doi:10.1080/03610929608831739

Choosing principal components for multivariate statistical process control

George C. Runger

Research output: Contribution to journal › Article › peer-review

37 Scopus citations

Abstract

Principal components are useful for multivariate process control. Typically, the principal component variables are often selected to summarize the variation in the process data. We provide an analysis to select the principal component variables to be included in a multivariate control chart that incorporates the unique aspects of the process control problem (rather than using traditional principal component guidelines).

Original language	English (US)
Pages (from-to)	909-922
Number of pages	14
Journal	Communications in Statistics - Theory and Methods
Volume	25
Issue number	5
DOIs	https://doi.org/10.1080/03610929608831739
State	Published - Jan 1 1996
Externally published	Yes

Keywords

Chi-square chart
Quality control
Selection of variables

ASJC Scopus subject areas

Statistics and Probability

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10.1080/03610929608831739

Cite this

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title = "Choosing principal components for multivariate statistical process control",

abstract = "Principal components are useful for multivariate process control. Typically, the principal component variables are often selected to summarize the variation in the process data. We provide an analysis to select the principal component variables to be included in a multivariate control chart that incorporates the unique aspects of the process control problem (rather than using traditional principal component guidelines).",

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year = "1996",

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AB - Principal components are useful for multivariate process control. Typically, the principal component variables are often selected to summarize the variation in the process data. We provide an analysis to select the principal component variables to be included in a multivariate control chart that incorporates the unique aspects of the process control problem (rather than using traditional principal component guidelines).

KW - Chi-square chart

KW - Quality control

KW - Selection of variables

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JO - Communications in Statistics - Theory and Methods

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IS - 5

ER -

Choosing principal components for multivariate statistical process control

Abstract

Keywords

ASJC Scopus subject areas

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