Árboles de clasificación para el análisis de gráficos de control multivariantes

In statistical quality control, one of the most widely used tools are the controlcharts. The main problem of the multivariate control charts lies in that they onlyindicate that a change in the process has happened, but they do not show whichvariable or variables are the source of this change. In the...

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Main Authors: Gámez Martínez, Matías, Alfaro Cortés, Esteban, Alfaro Navarro, José Luis, García Rubio, Noelia
Format: Artículo
Language: Español
Published: 2015
Online Access: http://revistas.ucr.ac.cr/index.php/matematica/article/view/1417
http://hdl.handle.net/10669/12945
Summary: In statistical quality control, one of the most widely used tools are the controlcharts. The main problem of the multivariate control charts lies in that they onlyindicate that a change in the process has happened, but they do not show whichvariable or variables are the source of this change. In the specialized literature thereare many approaches to tackle this problem, although the most usual consists on thedecomposition of the T2 statistic. In this research, we propose an alternative methodthrough the application of classification trees. The results show that this methodconstitutes a good tool to help to interpret the multivariate control charts.Keywords: Statistic Process Control, T2 Hotelling, Classification trees.