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A nonparametric control chart based on the Mann-Whitney statistic
Nonparametric or distribution-free charts can be useful in statistical process control problems when there is limited or lack of knowledge about the underlying process distribution. In this paper, a phase II Shewhart-type chart is considered for location, based on reference data from phase I analysis and the well-known Mann-Whitney statistic. Control limits are computed using Lugannani-Rice-saddlepoint, Edgeworth, and other approximations along with Monte Carlo estimation. The derivations take account of estimation and the dependence from the use of a reference sample. An illustrative numerical example is presented. The in-control performance of the proposed chart is shown to be much superior to the classical Shewhart X̄ chart. Further comparisons on the basis of some percentiles of the out-of-control conditional run length distribution and the unconditional out-of-control ARL show that the proposed chart is almost as good as the Shewhart X̄ chart for the normal distribution, but is more powerful for a heavy-tailed distribution such as the Laplace, or for a skewed distribution such as the Gamma. Interactive software, enabling a complete implementation of the chart, is made available on a website.
First available in Project Euclid: 1 April 2008
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ARL and run length percentiles conditioning method distribution-free Monte Carlo estimation parameter estimation phase I and phase II saddlepoint and edgeworth approximations Shewhart X̄ chart statistical process control
Copyright © 2008, Institute of Mathematical Statistics
Chakraborti, Subhabrata; van de Wiel, Mark A. A nonparametric control chart based on the Mann-Whitney statistic. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen, 156--172, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2008. doi:10.1214/193940307000000112. https://projecteuclid.org/euclid.imsc/1207058271
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