Nonparametric CUSUM and EWMA Control Charts for Detecting Mean Shifts

Nonparametric control charts are useful when the underlying process distribution is not likely to be normal or is unknown. In this paper, we propose two nonparametric analogs of the CUSUM and EWMA control charts based on the Wilcoxon rank-sum test for detecting process mean shifts. We first derive t...

Full description

Saved in:
Bibliographic Details
Published inJournal of quality technology Vol. 42; no. 2; pp. 209 - 226
Main Authors Li, Su-Yi, Tang, Loon-Ching, Ng, Szu-Hui
Format Journal Article
LanguageEnglish
Published Milwaukee, WI Taylor & Francis 01.04.2010
American Society for Quality
Taylor & Francis Ltd
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Nonparametric control charts are useful when the underlying process distribution is not likely to be normal or is unknown. In this paper, we propose two nonparametric analogs of the CUSUM and EWMA control charts based on the Wilcoxon rank-sum test for detecting process mean shifts. We first derive the run-length distributions of the proposed control charts and then compare the performance of the proposed nonparametric charts to (1) CUSUM and EWMA control charts on subgroup means and (2) the median chart and the Shewhart-type nonparametric control chart based on Mann-Whitney test. We show that the charts proposed herein perform well in detecting step mean shifts and perform almost the same as the parametric counterparts when the underlying process output follows a normal distribution and better when the output is nonnormal. We also study the effect of the reference sample size and the subgroup size on the performance of the proposed charts. A numerical example is also given as an illustration of the design and implementation of the proposed charts.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0022-4065
2575-6230
DOI:10.1080/00224065.2010.11917817