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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. Further, it discusses observations tied to the monitored population quantile, focusing on the Shewhart Sign chart. The book also addresses the issue of practically assuming the normality and the independence when a process is statistically monitored, and examines in detail change-point analysis-based distribution-free control charts designed for Phase I applications. Moreover, it introduces six distribution-free EWMA schemes for simultaneously monitoring the location and scale parameters of a univariate continuous process, and establishes two nonparametric Shewhart-type control charts based on order statistics with signaling runs-type rules. Lastly, the book proposes novel and effective method for early disease detection.
Preface
Recent Advances on Univariate Distribution-Free Shewhart-Type Control Charts
Introduction
Distribution-Free Control Charts Based on Order Statistics
Distribution-Free Control Charts Based on Sign Statistics
Distribution-Free Control Charts Based on Ranks
Multivariate Nonparametric Control Charts Based on Ordered Samples, Signs and Ranks
The Shewhart Sign Chart with Ties: Performance and Alternatives
Statistical Process Monitoring and the Issue of Assumptions in Practice: Normality and Independence
On Change-Point Analysis-Based Distribution-Free Control Charts with Phase I Applications
A Class of Distribution-Free Exponentially Weighted Moving Average Schemes for Joint Monitoring of Location and Scale Parameters
Distribution-Free Phase II Control Charts Based on Order Statistics with Runs-Rules
A Nonparametric Control Chart for Dynamic Disease Risk Monitoring