Deep learning-based residual control chart for count data

Jong Min Kim, Il Do Ha

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Statistical process control for count data has difficulty overcoming multicollinearity. In this paper, we propose a new deep learning residual control chart based on the asymmetrical count response variable when there are highly correlated explanatory variables. We implement and compare different methods such as neural network, deep learning, principal component analysis based Poisson regression, principal component analysis based negative binomial regression, nonlinear principal component analysis based Poisson regression, and nonlinear principal component analysis based negative binomial regression in terms of the root mean squared error. Using two asymmetrical simulated datasets generated by the combined multivariate normal, binary and copula functions, the neural network and deep learning have a smaller mean, median, and interquartile range when compared to the principal component analysis based Poisson regression, principal component analysis based negative binomial regression, nonlinear principal component analysis based Poisson regression, and nonlinear principal component analysis based negative binomial regression. We also compare the deep learning and neural network based residual control charts in terms of the average run length with the copula based asymmetrical simulated data and real takeover bids data.

Original languageEnglish (US)
Pages (from-to)370-381
Number of pages12
JournalQuality Engineering
Volume34
Issue number3
DOIs
StatePublished - 2022

Bibliographical note

Funding Information:
The authors would like to thank the editor and the two anonymous respected referees for their suggestions, which have greatly improved the paper. 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:
© 2022 Taylor & Francis Group, LLC.

Keywords

  • Average run length
  • count data
  • deep learning: residual control chart

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