An application of a generalized inverse regression estimator to Taguchi's T-Method

Abstract

Taguchi's T-method is a part of the Mahalanobis-Taguchi (MT) system. The T-method is a technique for making predictions, and it has applications in various fields, including economic forecasting and predicting demand. The T-method can be summarized as follows: Prepare many weak learners that each predict one output value. Then, use the signal to noise ratio (S/N) to perform a weighted integration of these predictions and derive an overall estimate. If the accuracies of each of the individual estimates are increased, the accuracy of the integrated estimate will be increased. Therefore, in this paper, we propose a way to improve the accuracies of the individual estimates. Our method uses the concept of a generalized inverse regression estimator for each of the individual estimators. We use Monte Carlo simulations under various conditions to compare the prediction accuracies of the proposed method and that of the existing method. In many cases, our results show that the prediction accuracy of the proposal method is better than that of the original T-method. In particular, when the number of samples is small, the prediction accuracy of the proposal method is much better than that of the original T-method. Thus, we conclude that our proposed method is effective.