Bootstrap-based T2 multivariate control charts

Poovich Phaladiganon, Seoung Bum Kim, Victoria C.P. Chen, Jun Geol Baek, Sun Kyoung Park

Research output: Contribution to journalArticlepeer-review

74 Citations (Scopus)


Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop univariate control charts to monitor a single process, no effort has been made to integrate the effectiveness of the bootstrap technique with multivariate control charts. In the present study, we propose a bootstrap-based multivariate T2 control chart that can efficiently monitor a process when the distribution of observed data is nonnormal or unknown. A simulation study was conducted to evaluate the performance of the proposed control chart and compare it with a traditional Hotelling's T2 control chart and the kernel density estimation (KDE)-based T2 control chart. The results showed that the proposed chart performed better than the traditional T2 control chart and performed comparably with the KDE-based T 2 control chart. Furthermore, we present a case study to demonstrate the applicability of the proposed control chart to real situations.

Original languageEnglish
Pages (from-to)645-662
Number of pages18
JournalCommunications in Statistics: Simulation and Computation
Issue number5
Publication statusPublished - 2011 May


  • Average run length
  • Bootstrap
  • Hotelling's T chart
  • Kernel density estimation
  • Multivariate control charts

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation


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