Control chart is a statistical process control tool to routinely monitor the quality of a process. In the past decade, using auxiliary information to enhance the ability of control chart to detect parameter shifts has attracted wide attention in literature. Most of the existing works in this topic use a linear model and assume performance and auxiliary variables follow a bivariate normal distribution to establish the auxiliary-information-based (AIB) charts. However, the normality assumption could be violated on some occasions. In this study, the skew-normal distribution is used to characterize the auxiliary variable to expand the joint distribution of the performance and auxiliary variables to a generalized joint family. Then, a new multivariate AIB chart is established to monitor the quality of process. The performance of the proposed AIB control chart method is verified using Monte Carlo simulations. An example with regard to the cement filling process is used for illustration.
Relation:
Journal of Statistical Computation and Simulation 92(3), p.645-666
