Árboles de clasificación para el análisis de gráficos de control multivariantes
In statistical quality control, one of the most widely used tools are the control charts. The main problem of the multivariate control charts lies in that they only indicate that a change in the process has happened, but they do not show which variable or variables are the source of this change. In...
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Universidad de Costa Rica
2009-02-01
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doaj-825d46b58b0d4219aef2490fa7faa9342020-11-25T00:59:57ZspaUniversidad de Costa RicaRevista de Matemática: Teoría y Aplicaciones2215-33732009-02-01161304210.15517/rmta.v16i1.14171349Árboles de clasificación para el análisis de gráficos de control multivariantesMatías Gámez Martínez0Esteban Alfaro Cortés1José Luis Alfaro Navarro2Noelia García Rubio3Universidad de Castilla, Facultad de Ciencias Económicas y Empresariales de AlbaceteUniversidad de Castilla, Facultad de Ciencias Económicas y Empresariales de AlbaceteUniversidad de Castilla, Facultad de Ciencias Económicas y Empresariales de AlbaceteUniversidad de Castilla, Facultad de Ciencias Económicas y Empresariales de AlbaceteIn statistical quality control, one of the most widely used tools are the control charts. The main problem of the multivariate control charts lies in that they only indicate that a change in the process has happened, but they do not show which variable or variables are the source of this change. In the specialized literature there are many approaches to tackle this problem, although the most usual consists on the decomposition of the T2 statistic. In this research, we propose an alternative method through the application of classification trees. The results show that this method constitutes a good tool to help to interpret the multivariate control charts. Keywords: Statistic Process Control, T2 Hotelling, Classification trees.https://revistas.ucr.ac.cr/index.php/matematica/article/view/1417 |
collection |
DOAJ |
language |
Spanish |
format |
Article |
sources |
DOAJ |
author |
Matías Gámez Martínez Esteban Alfaro Cortés José Luis Alfaro Navarro Noelia García Rubio |
spellingShingle |
Matías Gámez Martínez Esteban Alfaro Cortés José Luis Alfaro Navarro Noelia García Rubio Árboles de clasificación para el análisis de gráficos de control multivariantes Revista de Matemática: Teoría y Aplicaciones |
author_facet |
Matías Gámez Martínez Esteban Alfaro Cortés José Luis Alfaro Navarro Noelia García Rubio |
author_sort |
Matías Gámez Martínez |
title |
Árboles de clasificación para el análisis de gráficos de control multivariantes |
title_short |
Árboles de clasificación para el análisis de gráficos de control multivariantes |
title_full |
Árboles de clasificación para el análisis de gráficos de control multivariantes |
title_fullStr |
Árboles de clasificación para el análisis de gráficos de control multivariantes |
title_full_unstemmed |
Árboles de clasificación para el análisis de gráficos de control multivariantes |
title_sort |
árboles de clasificación para el análisis de gráficos de control multivariantes |
publisher |
Universidad de Costa Rica |
series |
Revista de Matemática: Teoría y Aplicaciones |
issn |
2215-3373 |
publishDate |
2009-02-01 |
description |
In statistical quality control, one of the most widely used tools are the control
charts. The main problem of the multivariate control charts lies in that they only
indicate that a change in the process has happened, but they do not show which
variable or variables are the source of this change. In the specialized literature there
are many approaches to tackle this problem, although the most usual consists on the
decomposition of the T2 statistic. In this research, we propose an alternative method
through the application of classification trees. The results show that this method
constitutes a good tool to help to interpret the multivariate control charts.
Keywords: Statistic Process Control, T2 Hotelling, Classification trees. |
url |
https://revistas.ucr.ac.cr/index.php/matematica/article/view/1417 |
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