Distribution-Free Methods for Statistical Process Monitoring and Control
This book explores nonparametric statistical process control. It provides an up-to-date overview of nonparametric Shewhart-type univariate control charts, and reviews the recent literature on nonparametric charts, particularly multivariate schemes. Furthe
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Distribution-Free Methods for Statistical Process Monitoring and Control
Distribution-Free Methods for Statistical Process Monitoring and Control
Markos V. Koutras Ioannis S. Triantafyllou •
Editors
Distribution-Free Methods for Statistical Process Monitoring and Control
123
Editors Markos V. Koutras Department of Statistics and Insurance Science University of Piraeus Piraeus, Greece
Ioannis S. Triantafyllou Department of Computer Science and Biomedical Informatics University of Thessaly Volos, Greece
ISBN 978-3-030-25080-5 ISBN 978-3-030-25081-2 https://doi.org/10.1007/978-3-030-25081-2
(eBook)
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Preface
Statistical process control is widely used to monitor the quality of the final product of a process. In any production process, no matter how carefully it is maintained, a natural variability is always present. Control charts facilitate the practitioners to identify assignable causes so that corrective actions are carried out and the process is restored to the desirable in-control state. In most control charts, the process output is assumed to follow a specified probability distribution (usually normal); therefore the techniques applied for them are parametric ones and are affected by the distributional assumption used each time. However, this assumption may not be fulfilled in practice and therefore the resulting control charts cannot be applied, or, if applied, may not be accurate enough. Therefore, the development of nonparametric methods which can be efficiently used for hypothesis-testing problems without making any specific assumptions about the distribution of the underlying process is crucial. Th
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