Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model

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 (<i<r</i<) control chart for binary asymmetrical (non-symmetric) data with multicollinearity between input variables via combining prin...
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Autor*in:	Jong-Min Kim [verfasserIn] Ning Wang [verfasserIn] Yumin Liu [verfasserIn] Kayoung Park [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	residual control chart binary data pca fpca multicollinearity

Übergeordnetes Werk:	In: Symmetry - MDPI AG, 2009, 12(2020), 3, p 381
Übergeordnetes Werk:	volume:12 ; year:2020 ; number:3, p 381

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DOI / URN:	10.3390/sym12030381

Katalog-ID:	DOAJ085273570

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spelling	10.3390/sym12030381 doi (DE-627)DOAJ085273570 (DE-599)DOAJ5a5ce7fcd71f48feae48e8947088ae31 DE-627 ger DE-627 rakwb eng QA1-939 Jong-Min Kim verfasserin aut Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (<i<r</i<) 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 <i<r</i< control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits. residual control chart binary data pca fpca multicollinearity Mathematics Ning Wang verfasserin aut Yumin Liu verfasserin aut Kayoung Park verfasserin aut In Symmetry MDPI AG, 2009 12(2020), 3, p 381 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:12 year:2020 number:3, p 381 https://doi.org/10.3390/sym12030381 kostenfrei https://doaj.org/article/5a5ce7fcd71f48feae48e8947088ae31 kostenfrei https://www.mdpi.com/2073-8994/12/3/381 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 3, p 381
allfields_unstemmed	10.3390/sym12030381 doi (DE-627)DOAJ085273570 (DE-599)DOAJ5a5ce7fcd71f48feae48e8947088ae31 DE-627 ger DE-627 rakwb eng QA1-939 Jong-Min Kim verfasserin aut Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (<i<r</i<) 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 <i<r</i< control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits. residual control chart binary data pca fpca multicollinearity Mathematics Ning Wang verfasserin aut Yumin Liu verfasserin aut Kayoung Park verfasserin aut In Symmetry MDPI AG, 2009 12(2020), 3, p 381 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:12 year:2020 number:3, p 381 https://doi.org/10.3390/sym12030381 kostenfrei https://doaj.org/article/5a5ce7fcd71f48feae48e8947088ae31 kostenfrei https://www.mdpi.com/2073-8994/12/3/381 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 3, p 381
allfieldsGer	10.3390/sym12030381 doi (DE-627)DOAJ085273570 (DE-599)DOAJ5a5ce7fcd71f48feae48e8947088ae31 DE-627 ger DE-627 rakwb eng QA1-939 Jong-Min Kim verfasserin aut Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (<i<r</i<) 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 <i<r</i< control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits. residual control chart binary data pca fpca multicollinearity Mathematics Ning Wang verfasserin aut Yumin Liu verfasserin aut Kayoung Park verfasserin aut In Symmetry MDPI AG, 2009 12(2020), 3, p 381 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:12 year:2020 number:3, p 381 https://doi.org/10.3390/sym12030381 kostenfrei https://doaj.org/article/5a5ce7fcd71f48feae48e8947088ae31 kostenfrei https://www.mdpi.com/2073-8994/12/3/381 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 3, p 381
allfieldsSound	10.3390/sym12030381 doi (DE-627)DOAJ085273570 (DE-599)DOAJ5a5ce7fcd71f48feae48e8947088ae31 DE-627 ger DE-627 rakwb eng QA1-939 Jong-Min Kim verfasserin aut Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (<i<r</i<) 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 <i<r</i< control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits. residual control chart binary data pca fpca multicollinearity Mathematics Ning Wang verfasserin aut Yumin Liu verfasserin aut Kayoung Park verfasserin aut In Symmetry MDPI AG, 2009 12(2020), 3, p 381 (DE-627)610604112 (DE-600)2518382-5 20738994 nnns volume:12 year:2020 number:3, p 381 https://doi.org/10.3390/sym12030381 kostenfrei https://doaj.org/article/5a5ce7fcd71f48feae48e8947088ae31 kostenfrei https://www.mdpi.com/2073-8994/12/3/381 kostenfrei https://doaj.org/toc/2073-8994 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2020 3, p 381
language	English
source	In Symmetry 12(2020), 3, p 381 volume:12 year:2020 number:3, p 381
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container_title	Symmetry
authorswithroles_txt_mv	Jong-Min Kim @@aut@@ Ning Wang @@aut@@ Yumin Liu @@aut@@ Kayoung Park @@aut@@
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callnumber-first	Q - Science
author	Jong-Min Kim
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topic_title	QA1-939 Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model residual control chart binary data pca fpca multicollinearity
topic	misc QA1-939 misc residual control chart misc binary data misc pca misc fpca misc multicollinearity misc Mathematics
topic_unstemmed	misc QA1-939 misc residual control chart misc binary data misc pca misc fpca misc multicollinearity misc Mathematics
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title	Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model
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title_auth	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 (<i<r</i<) 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 <i<r</i< control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.
abstractGer	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 (<i<r</i<) 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 <i<r</i< control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.
abstract_unstemmed	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 (<i<r</i<) 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 <i<r</i< control chart is relatively efficient to monitor the simulated and real binary response data with the narrow length of control limits.
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title_short	Residual Control Chart for Binary Response with Multicollinearity Covariates by Neural Network Model
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