Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring

Kazuya Nishimura, Shun Matsuura, Hideo Suzuki

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In multivariate statistical process control, when a process shift occurs, not all variables but a few variables may shift from the in-control state. This paper proposes a multivariate EWMA control chart based on a variable selection using AIC.

Original languageEnglish
Pages (from-to)7-13
Number of pages7
JournalStatistics and Probability Letters
Volume104
DOIs
Publication statusPublished - 2015 Sep 1

Fingerprint

EWMA Chart
Process Monitoring
Control Charts
Variable Selection
Multivariate Statistical Process Control
Control charts
Process monitoring
Exponentially weighted moving average
Variable selection

Keywords

  • Akaike information criterion
  • Exponentially weighted moving average
  • Multivariate control chart
  • Multivariate statistical process control
  • Variable selection

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

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title = "Multivariate EWMA control chart based on a variable selection using AIC for multivariate statistical process monitoring",
abstract = "In multivariate statistical process control, when a process shift occurs, not all variables but a few variables may shift from the in-control state. This paper proposes a multivariate EWMA control chart based on a variable selection using AIC.",
keywords = "Akaike information criterion, Exponentially weighted moving average, Multivariate control chart, Multivariate statistical process control, Variable selection",
author = "Kazuya Nishimura and Shun Matsuura and Hideo Suzuki",
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AU - Nishimura, Kazuya

AU - Matsuura, Shun

AU - Suzuki, Hideo

PY - 2015/9/1

Y1 - 2015/9/1

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KW - Exponentially weighted moving average

KW - Multivariate control chart

KW - Multivariate statistical process control

KW - Variable selection

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