This contributed book focuses on major aspects of statistical quality control, shares insights into important new developments in the field, and adapts established statistical quality control methods for use in e.g. big data, network analysis and medical applications. The content is divided into two parts, the first of which mainly addresses statistical process control, also known as statistical process monitoring. In turn, the second part explores selected topics in statistical quality control, including measurement uncertainty analysis and data quality.
The peer-reviewed contributions gathered here were originally presented at the 13th International Workshop on Intelligent Statistical Quality Control, ISQC 2019, held in Hong Kong on August 12-14, 2019. Taken together, they bridge the gap between theory and practice, making the book of interest to both practitioners and researchers in the field of statistical quality control.
Part I Statistical Process Control.
Chapter 1.- Use of the Conditional
False Alarm Metric in Statistical Process Monitoring.
Chapter 2 Design
Considerations and Tradeoffs for Shewhart Control Charts.
Chapter 3 On the
Calculation of the ARL for Beta EWMA Control Charts.
Chapter 4 Flexible
Monitoring Methods for High-Yield Processes.
Chapter 5 An Average Loss
Control Chart Under a Skewed Process Distribution.
Chapter 6 ARL-unbiased
CUSUM schemes to monitor binomial counts.
Chapter 7 Statistical Aspects of
Target Setting for Attribute Data Monitoring.
Chapter 8 MAV control charts
for monitoring two-state processes using indirectly observed binary data.-
Chapter 9 Monitoring Image Processes Overview and Comparison Study.-
Chapter 10 Parallelized Monitoring of Dependent Spatiotemporal Processes.-
Chapter 11 Products Warranty Claim Monitoring under Variable Intensity
Rates.
Chapter 12 A Statistical (Process Monitoring) Perspective on Human
Performance Modeling in the Age of Cyber-Physical Systems.
Chapter 13
Monitoring Performance of Surgeons Using a New Risk-adjusted Exponentially
Weighted Moving Average Control Chart.
Chapter 14 Exploring the usefulness
of Functional Data Analysis for Health Surveillance.
Chapter 15 Rapid
Detection of Hot-spot by Tensor Decomposition with Application to Weekly
Gonorrhea Data.
Chapter 16 An approach to monitoring time between events
when events are frequent.- Part II Selected Topics from Statistical Quality
Control.
Chapter 17 Analysis of Measurement Precision Experiment with
Ordinal Categorical Variables.
Chapter 18 Assessing a Binary Measurement
System with Operator and Random Part Effects.
Chapter 19 Concepts, Methods
and Tools Enabling Measurement Quality.
Chapter 20 Assessing laboratory
effects in key comparisons with two transfer standards measured in two
petals: A Bayesian approach.
Chapter 21 Quality control activities are a
challenge for reducing variability.
Chapter 22 Is the Benford Law useful for
Data Quality Assessment?
Sven Knoth is a Professor of Computational Statistics at the Helmut Schmidt University, the University of the Federal Armed Forces, Hamburg, Germany. His main research areas include statistical process control, implementation of statistical algorithms in software, and applications of statistics in engineering. He has authored more than 60 research papers and he is an Associate Editor of the journals Computational Statistics and Quality Engineering.
Wolfgang Schmid is a Professor of Statistics at the European University Viadrina, Frankfurt (Oder), Germany. His main research areas include statistical process control, statistics in finance, spatial statistics, and environmetrics. He has authored more than 160 research papers and he is an Associate Editor of Sequential Analysis, AStA Advances in Statistical Analysis, and Journal of Multivariate Analysis. Between 2012-2020 he was the President of the German Statistical Society.
