Gower distance-based multivariate control charts for a mixture of continuous and categorical variables

Gulanbaier Tuerhong, Seoung Bum Kim

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Processes characterized by high dimensional and mixture data challenge traditional statistical process control charts. In this study, we propose a multivariate control chart based on the Gower distance that can handle a mixture of continuous and categorical data. An extensive simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compared it with some existing multivariate control charts. The simulation results revealed that the proposed control chart outperformed the existing charts when the number of categorical variables increases. Furthermore, we demonstrated the applicability and effectiveness of the proposed control charts through a real case study.

Original languageEnglish
Pages (from-to)1701-1707
Number of pages7
JournalExpert Systems with Applications
Volume41
Issue number4 PART 2
DOIs
Publication statusPublished - 2014 Jan 1

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Keywords

  • Gower distance
  • Mixture data
  • Multivariate control charts
  • Quality control
  • Statistical process control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

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