### Abstract

If the number of false alarms when the process is in control is held constant, the most sensitive procedures for detecting the out-of-control state are those that plot a subgroup statistic that is sensitive to outliers (e.g., mean or range) but determine the control limits in a resistant fashion. Ordinary charting procedures, such as the standard X̄ and R charts, perform less well, and the worst performance is turned in by procedures in which the subgroup statistics are themselves resistant (e.g., median charts). To illustrate the point that robustness depends not only on resistance of the statistical tools to outliers but also on the purpose of the analysis, robust cumulative sum charts are briefly discussed.

Original language | English (US) |
---|---|

Pages (from-to) | 173-184 |

Number of pages | 12 |

Journal | Technometrics |

Volume | 31 |

Issue number | 2 |

State | Published - May 1989 |

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### ASJC Scopus subject areas

- Mathematics(all)
- Statistics and Probability

### Cite this

**Robust control charts.** / Rocke, David M.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - Robust control charts

AU - Rocke, David M

PY - 1989/5

Y1 - 1989/5

N2 - If the number of false alarms when the process is in control is held constant, the most sensitive procedures for detecting the out-of-control state are those that plot a subgroup statistic that is sensitive to outliers (e.g., mean or range) but determine the control limits in a resistant fashion. Ordinary charting procedures, such as the standard X̄ and R charts, perform less well, and the worst performance is turned in by procedures in which the subgroup statistics are themselves resistant (e.g., median charts). To illustrate the point that robustness depends not only on resistance of the statistical tools to outliers but also on the purpose of the analysis, robust cumulative sum charts are briefly discussed.

AB - If the number of false alarms when the process is in control is held constant, the most sensitive procedures for detecting the out-of-control state are those that plot a subgroup statistic that is sensitive to outliers (e.g., mean or range) but determine the control limits in a resistant fashion. Ordinary charting procedures, such as the standard X̄ and R charts, perform less well, and the worst performance is turned in by procedures in which the subgroup statistics are themselves resistant (e.g., median charts). To illustrate the point that robustness depends not only on resistance of the statistical tools to outliers but also on the purpose of the analysis, robust cumulative sum charts are briefly discussed.

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UR - http://www.scopus.com/inward/citedby.url?scp=0024656838&partnerID=8YFLogxK

M3 - Article

VL - 31

SP - 173

EP - 184

JO - Technometrics

JF - Technometrics

SN - 0040-1706

IS - 2

ER -