Year-end Sale % OFF 
Abstract
Quality control studies have dealt with symmetrical data having the same shape with respect to left and right. In this research, we propose the residual (r) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining principal component analysis (PCA), functional PCA (FPCA) and the generalized linear model with probit and logit link functions, and neural network regression model. The motivation in this research is that the proposed control chart method can deal with both high-dimensional correlated multivariate data and high frequency functional multivariate data by neural network model and FPCA. We show that the neural network r control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.
Highlights
The current available quality control research has focused on symmetrical data having the same shape with respect to left and right
For the in-control and the one-inflated cases, the average run length (ARL) based on principal component analysis (PCA) are smaller than the ARLs based on functional PCA (FPCA) but, for zero-inflated case, the ARLs based on FPCA are smaller than the ARLs based on PCA
We have presented the binary response regression model-based statistical process control r-charts for dispersed binary asymmetrical data with multicollinearity among input variables
Summary
The current available quality control research has focused on symmetrical data having the same shape with respect to left and right. In order to monitor a process mean vector, there have been a number of multivariate control charts including Hotelling T 2 distribution [1], mulvariate CUSUM [2] and multivariate EWMA [3] These current available multivariate control charts have limitations to handle high-dimensional data because of the complexity of the covariance structure. Neural network based methods have been applied to quality control research areas, but there is no research available for residual (r) control charts for binary asymmetrical data with highly correlated multivariate covariates by using neural network regression model. The first one is that the proposed control chart method can deal with high-dimensional correlated multivariate data by neural network model and the second one Symmetry 2020, 12, 381; doi:10.3390/sym12030381 www.mdpi.com/journal/symmetry
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
