Residual control chart for binary response with multicollinearity covariates by neural network model

Jong Min Kim; Ning Wang; Yumin Liu; Kayoung Park

doi:10.3390/sym12030381

Residual control chart for binary response with multicollinearity covariates by neural network model

Jong Min Kim, Ning Wang, Yumin Liu, Kayoung Park

Statistics (Morris)

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

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.

Original language	English (US)
Article number	381
Journal	Symmetry
Volume	12
Issue number	3
DOIs	https://doi.org/10.3390/sym12030381
State	Published - Mar 1 2020

Bibliographical note

Funding Information:
J.-M.K. designed the model, analyzed the data and wrote the paper. N.W. formulated the conceptual framework, designed the model, obtained inference and wrote the paper. Y.L. formulated the conceptual framework, designed the model, obtained inference and wrote the paper. K. P analyzed the data and provided editorial supports. All the authors cooperated to revise the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China Grant (No. 71672182, No. U1604262 and No. U1904211).

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

Binary data
FPCA
Multicollinearity
PCA
Residual Control Chart

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10.3390/sym12030381

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title = "Residual control chart for binary response with multicollinearity covariates by neural network model",

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.",

keywords = "Binary data, FPCA, Multicollinearity, PCA, Residual Control Chart",

author = "Kim, {Jong Min} and Ning Wang and Yumin Liu and Kayoung Park",

note = "Funding Information: J.-M.K. designed the model, analyzed the data and wrote the paper. N.W. formulated the conceptual framework, designed the model, obtained inference and wrote the paper. Y.L. formulated the conceptual framework, designed the model, obtained inference and wrote the paper. K. P analyzed the data and provided editorial supports. All the authors cooperated to revise the paper. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China Grant (No. 71672182, No. U1604262 and No. U1904211). Publisher Copyright: {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.",

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doi = "10.3390/sym12030381",

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AU - Wang, Ning

AU - Liu, Yumin

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PY - 2020/3/1

Y1 - 2020/3/1

N2 - 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.

AB - 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.

KW - Binary data

KW - FPCA

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KW - Residual Control Chart

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Residual control chart for binary response with multicollinearity covariates by neural network model

Abstract

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Keywords

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