Master's Thesis at the University of Basra Explores Bootstrap for Estimating Taguchi's Loss Function and Signal-to-Noise Ratio

Master's Thesis at the University of Basra Explores Bootstrap for Estimating Taguchi's Loss Function and Signal-to-Noise Ratio

A master's thesis at the College of Administration and Economics, University of Basra, explored the use of the Bootstrap method for estimating Taguchi's Loss Function and the Signal-to-Noise Ratio (SNR), with practical application.

The thesis, submitted by student Duaa Thajil Kadhim, aimed to evaluate the efficiency of the Bootstrap method in estimating Taguchi's Loss Function and SNR, considered important measures of performance quality. This was achieved by comparing five confidence interval methods and testing their accuracy using Monte Carlo simulations, applying them to real-world healthcare data.

The thesis included selecting an appropriate confidence interval, analyzing the results, and arriving at a set of conclusions and recommendations.

The thesis concluded that the accuracy and reliability of the estimation improve with increasing sample size up to a certain level, and that intermediate sizes achieve a better balance between estimation accuracy and coverage. The BCa method also demonstrated superior performance compared to the other methods. The letter recommended adopting the BCa method in analyzing health data and expanding the use of the Bootstrap technique, while monitoring indicators with high variance and working to improve them.