Control Charts for Dependent and Independent Measurements Based on Bootstrap Methods

Regina Y. Liu, Jen Tang

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

Shewhart charts are widely accepted as standard tools for monitoring manufacturing processes of univariate, independent “nearly” normal measurements. They are not as well developed beyond these types of data. We generalize the idea of Shewhart charts to cover other types of data commonly encountered in practice. More specifically, we develop some valid control charts for dependent data and for independent data that are not necessarily “nearly” normal. We derive the proposed charts from the moving blocks bootstrap and the standard bootstrap methods. Their constructions are completely nonparametric no distributional assumptions are required. Some simulated as well as real data examples are included they are very supportive of the proposed methods.

Original languageEnglish (US)
Pages (from-to)1694-1700
Number of pages7
JournalJournal of the American Statistical Association
Volume91
Issue number436
DOIs
StatePublished - Dec 1 1996

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Dependent processes
  • Moving blocks bootstrap
  • Nonparametric process control
  • Shewhart charts

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