Improved bootstrap X¯ control chart for non-normally distributed data

This article proposes new method to improve the performance of bootstrap control chart for non-normal data. Bootstrap control charts for monitoring data require attention because the average run length (ARL) results of the bootstrap control charts can be less accurate and lacks stability. To deal with this issue, X¯ control chart from non-normal distribution was proposed using a novel improved Bootstrap method. The control limit is constructed using improved bootstrap to ensure that the UCL and LCL obtained actually give the desired ARL₀ value. Comparison of performance is studied among the Bajgier bootstrap control chart, Liu Tang Bootstrap, and their Improved bootstrap control chart. The result show that the proposed bootstrap control chart has better accuracy than its classic method. Key points: • A novel improved bootstrap method is proposed to deal with the ARL results of the bootstrap control charts that can vary. • The new X¯ Control Chart from non-normal distribution was constructed using a novel improved bootstrap method.