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New statistical process control charts for overdispersed count data based on the Bell distribution
14 references·1 citing·41 second hop
New statistical process control charts for overdispersed count data based on the Bell distribution (2023)New statistical process control charts fo…A progressive mean control chart for COM-Poisson distribution (2019)A progressive mean control ch…Control Charts for Dispersed Count Data: An Overview (2014)Control Charts for Dispersed …Synthetic Phase II Shewhart‐type Attributes Control Charts When Process Parameters are Estimated (2013)Synthetic Phase II Shewhart‐t…DESIGN OF THE c AND np CHARTS WHEN THE PARAMETERS ARE ESTIMATED (2012)DESIGN OF THE c AND np CHARTS…A generalized statistical control chart for over‐ or under‐dispersed data (2011)A generalized statistical con…Control charts for health care monitoring under overdispersion (2009)Control charts for health car…Properties and performance of thec-chart for attributes data (2008)Properties and performance of…actuar: AnRPackage for Actuarial Science (2008)actuar: AnRPackage for Actuar…Overdispersion in health care performance data: Laney's approach (2006)Overdispersion in health care…Effects of Parameter Estimation on Control Chart Properties: A Literature Review (2006)Effects of Parameter Estimati…Run length distributions for estimated attributes charts (1999)Run length distributions for …Statistical control charts for shifted generalized poisson distribution (1994)Statistical control charts fo…Statistical Control Charts Based on a Geometric Distribution (1992)Statistical Control Charts Ba…Introduction to Statistical Quality Control (1992)Introduction to Statistical Q…Statistical process control of overdispersed count data based on one‐parameter Poisson mixture models (2022)Statistical process control o…An introduction to mathematical risk theory (1979)Loss Models: From Data to Decisions (1999)Modern Actuarial Risk Theory (2008)Composite Lognormal–Pareto model with random threshold (2010)New composite models for the Danish fire insurance data (2012)Modeling loss data using composite models (2014)A review of multivariate control charts (1995)Cumulative Sum Charts and Charting for Quality Improvement (1998)Introduction to Statistical Quality Control (2005)Current research on profile monitoring (2007)An Overview of Phase I Analysis for Process Improvement and Monitoring (2014)Some Current Directions in the Theory and Application of Statistical Process Monitoring (2014)CONTINUOUS INSPECTION SCHEMES (1954)Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements (1990)Monitoring the Coefficient of Variation Using the Side Sensitive Group Runs Chart (2015)Generalized linear model based monitoring methods for high‐yield processes (2020)Multivariate time-between-events monitoring: An overview and some overlooked underlying complexities (2020)X- and R-Chart Control Limits Based on a Small Number of Subgroups (1969)The run length distributions of the R, s and s2 control charts when is estimated (1998)The performance of the hypergeometric np chart with estimated parameter (2021)Statistical Quality Control (1990)The Effect of Sample Size on Estimated Limits forXandXControl Charts (1993)Control Charts Based on Attribute Data: Bibliography and Review (1997)The effect of parameter estimation on phase II monitoring of poisson regression profiles (2018)Introduction to Statistical Quality Control. (1986)Detecting a Shift in Fraction Nonconforming Using Run-Length Control Charts with 100% Inspection (1991)Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part I Introduction and Basic Theory (1998)Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues (1998)Control Charts Based on the Exponential Distribution: Adapting Runs Rules for the t Chart (2013)Understanding Statistical Process Control (1986)Statistical process control as a tool for research and healthcare improvement (2003)The Use of Stories in Clinical Research and Health Policy (2005)A new control chart for exponential distributed life using EWMA (2014)A new sampling plan under the exponential distribution (2016)A Multivariate Control Chart for Monitoring Several Exponential Quality Characteristics Using EWMA (2018)Self-Starting Monitoring Scheme for Poisson Count Data With Varying Population Sizes (2015)Financial impacts of innovation in Six Sigma projects (2019)A Two‐Sided Cumulative Sum Chart for First‐Order Integer‐Valued Autoregressive Processes of Poisson Counts (2012)A Flexible and Generalized Exponentially Weighted Moving Average Control Chart for Count Data (2013)Time-Series Modeling for Statistical Process Control (1988)Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications (2014)
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References

  • 1992Introduction to Statistical Quality Control7,842 cited
  • 2006Effects of Parameter Estimation on Control Chart Properties: A Literature Review586 cited
  • 1992Statistical Control Charts Based on a Geometric Distribution180 cited
  • 2008actuar: AnRPackage for Actuarial Science124 cited
  • 2011A generalized statistical control chart for over‐ or under‐dispersed data63 cited
  • 2014Control Charts for Dispersed Count Data: An Overview60 cited
  • 2012DESIGN OF THE c AND np CHARTS WHEN THE PARAMETERS ARE ESTIMATED47 cited
  • 2006Overdispersion in health care performance data: Laney's approach46 cited
  • 1999Run length distributions for estimated attributes charts37 cited
  • 2008Properties and performance of thec-chart for attributes data34 cited
  • 2013Synthetic Phase II Shewhart‐type Attributes Control Charts When Process Parameters are Estimated27 cited
  • 1994Statistical control charts for shifted generalized poisson distribution23 cited
  • 2009Control charts for health care monitoring under overdispersion17 cited
  • 2019A progressive mean control chart for COM-Poisson distribution16 cited

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  • 2022Statistical process control of overdispersed count data based on one‐parameter Poisson mixture models

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6 cited