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Monitoring the Mean Vector and the Covariance Matrix of Bivariate Processes

Autor(es):
Guerreiro Machado, Marcela Aparecida ; Branco Costa, Antonio Fernando
Número total de Autores: 2
Tipo de documento: Artigo Científico
Fonte: BRAZILIAN JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT; v. 5, n. 1, p. 16-pg., 2008-07-01.
Resumo

This paper proposes the joint use of two charts based on the non-central chi-square statistic (NCS statistic) for monitoring the mean vector and the covariance matrix of bivariate processes, named as the joint NCS charts. The expression to compute the ARL, which is defined as the average number of samples the joint charts need to signal an out-of-control condition, is derived. The joint NCS charts might be more sensitive to changes in the mean vector or, alternatively, more sensitive to changes in the covariance matrix, accordingly to the values of their design parameters. In general, the joint NCS charts are faster than the combined T-2 and vertical bar S vertical bar charts in signaling out-of-control conditions. Once the proposed scheme signals, the user can immediately identify the out-of-control variable. The risk of misidentifying the out-of-control variable is small (less than 5.0%). (AU)

Processo FAPESP: 06/00491-0 - Novas estratégias de monitoramento para processos multivariados
Beneficiário:Marcela Aparecida Guerreiro Machado de Freitas
Modalidade de apoio: Bolsas no Brasil - Doutorado