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Control Charts for Dependent and Independent Measurements Based on Bootstrap Methods
Regina Y. Liu and Jen Tang
Journal of the American Statistical Association
Vol. 91, No. 436 (Dec., 1996), pp. 1694-1700
Stable URL: http://www.jstor.org/stable/2291598
Page Count: 7
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Shewhart charts are widely accepted as standard tools for monitoring manufacturing processes of univariate, independent, and "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, and no distributional assumptions are required. Some simulated as well as real data examples are included, and they are very supportive of the proposed methods.
Journal of the American Statistical Association © 1996 American Statistical Association