Percentile bootstrap control chart for monitoring process variability under non-normal processes

Document Type : Article


1 College of Statistical and Actuarial Sciences, University of the Punjab Lahore-54000, Pakistan

2 GC University Faisalabad, Faisalabad, Pakistan

3 Department of Statistic, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia


In the recent years, another approach named as the bootstrap method is getting popular in Statistical Process Control (SPC) specifically when the underlying distribution of the process is unknown. The bootstrap estimators are getting popularity in statistical process control due to their remarkable properties for non-normal distribution. In this paper the bootstrap control chart is developed for monitoring process variability and robustness is discussed through simulation studies. It appears that the proposed control chart for monitoring process variability based on the bootstrap method is performing better to detect out-of-control signal in a case when data follow skewed distributions. Therefore, the proposed chart is more recommendable for industrial practitioners.


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