Deep learning-based residual control chart for binary response

Jong Min Kim, Il Do Ha

Research output: Contribution to journalArticlepeer-review

Abstract

A residual (r) control chart of asymmetrical and non-normal binary response variable with highly correlated explanatory variables is proposed in this research. To avoid multicollinearity between multiple explanatory variables, we employ and compare a neural network regression model and deep learning regression model using Bayesian variable selection (BVS), principal component analysis (PCA), nonlinear PCA (NLPCA) or whole multiple explanatory variables. The advantage of our r control chart is able to process both non-normal and correlated multivariate explanatory variables by employing a neural network model and deep learning model. We prove that the deep learning r control chart is relatively efficient to monitor the simulated and real binary response asymmetric data compared with r control chart of the generalized linear model (GLM) with probit and logit link functions and neural network r control chart.

Original languageEnglish (US)
Article number1389
JournalSymmetry
Volume13
Issue number8
DOIs
StatePublished - Jul 31 2021

Bibliographical note

Funding Information:
Funding: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2020R1F1A1A01056987).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Bayesian variable selection
  • Binary data
  • Nonlinear PCA
  • PCA
  • Residual control chart

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