基礎研究

Durbin-Watson test model

1. Rebuilding Durbin-Watson critical values in the model of Yt=et (Simplified Chinese Edition)

2. Rebuilding Durbin-Watson critical values in the model of Yt=et with sufficient statisitc (Simplified Chinese Edition)

3. Rebuilding Durbin-Watson critical values in first-order autoregressive and linear model of Yt=μ+εt

4. Rebuilding Durbin-Watson critical values in first-order autoregressive and linear model of Yt=μ+εt with sufficient statisitc

5. Rebuilding Durbin-Watson critical values in first-order autoregressive and simple linear model of Yt=βtXt+εt by GLS

6. Rebuilding Durbin-Watson critical values in the first-order autoregressive and simple linear model of Yt=βtXt+εt with sufficient statisitc by GLS

7. Rebuilding Durbin-Watson critical values in first-order autoregressive and simple linear model of Yt=βtXt+εt by MME

8. Rebuilding Durbin-Watson critical values in the first-order autoregressive and simple linear model of Yt=βtXt+εt with sufficient statisitc by MME

9. Rebuilding Durbin-Watson critical values in the first-order autoregressive and simple linear model of Yt=β0+βtXt+εt

10. Rebuilding Durbin-Watson critical values in the first-order autoregressive and simple linear model of Yt=β0+βtXt+εt with sufficient statisitc

11. Rebuilding Durbin-Watson critical values in the first-order autoregressive and multiple regressive model

12. Rebuilding Durbin-Watson critical values in the first-order autoregressive and multiple regressive model with sufficient statisitc

13. Rebuilding Durbin-Watson critical values in the first-order autoregressive and multiple regressive model without intercept

14. Rebuilding Durbin-Watson critical values in the first-order autoregressive and multiple regressive model with sufficient statistic and without intercept

中央極限定理

1. Numerical Simulation of Sample Mean and Variance Under Central Limit Theorem

2. Central limit theorem of AR(1) model from Durbin-Watson test

最短區間估計法

基於Curvilinear lines combined method的實驗設計方法

有關「實驗設計一類因子分析可用Curvilinear lines-combined 方法解決違反假設問題、免除 lack-of-fit 檢定使用、從比較異同提高到類別項排序規律」之相關論文，可應用在語言AI建模、各國平均每人所得年資料、財報指標依產業或季度等方面。

應用研究