本研究透過copula entropy的應用，優化stepwise變數選取方法，並將其應用於B-spline函數中來近似實際函數，以挑選出必要的節點，同時又能減少與實際函數間的誤差。此方法利用copula entropy的獨特特性，以及RCV(refitted cross-validation)變異數估計方法，改善了stepwise變數選取的方法，此外，我們將改進方法與其他方法進行比較，以驗證其在實際應用中的效能表現。實驗結果顯示，此方法在回歸函數上誤差和準確率方面優於其他常見的變數選取方法，在近似spline函數上於部分情況中也表現出較佳的節點挑選效果，進而在B-spline函數的應用中實現更有效率的節點選擇。

In this study, we optimize the stepwise variable selection method through the application of copula entropy and apply it to the B-spline function to approximate the actual function in order to pick out the necessary knots and at the same time reduce the error with the actual function. This method improves the stepwise variable selection method by utilizing the unique characteristics of copula entropy and the RCV (refitted cross-validation) estimation method. In addition, we compare the improved method with other methods to verify its performance in practical applications. The experimental results show that this method outperforms other common variable selection methods in terms of error and accuracy on the regression function, and also shows better knot selection on the approximate spline function in some cases, which leads to more efficient knot selection in the application of the B-spline function.

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