Quality Control Charts for EWMA with Wavelet Shrinkage: A Simulation Study

The study aims to utilise modern statistical techniques to analyze data often sensitive to small changes. Quality control charts are valuable tools for monitoring and analyzing data and identifying and extracting points that fall outside the control limits. On the other hand, wavelet shrinkage is an effective technique for removing noise from data and identifying deviations while preserving significant signals, thus enhancing the accuracy of the analysis. Alongside using actual data that represents the qualitative feature of children's weights- a crucial indicator for evaluating overall health and early detection of nutritional or health issues- this chart was simulated using MATLAB. However, weight measurements are frequently susceptible to noise and interference due to factors such as measurement errors or natural variations. The study concluded that the proposed chart (weighted moving average using wavelet shrinkage) is superior to the classical chart because it performs better in identifying the differences between the upper and lower limits of the two charts. We also observed that the difference between the two limits in the proposed chart is smaller