臺灣博碩士論文加值系統

許多研究學者發現當利用徹底搜尋法的準則來建構迴歸樹時，會因為解釋變數的切割點數量不同，對於切割變數的選取存在偏差。我們於本論文中發現，
在非空間性或空間性的區域經濟資料下，透過建構迴歸樹模型的方法來劃分俱樂部收斂
(Convergence club) 區域時，切割變數的選取偏差現象依然存在。我們利用
殘差分析的準則來建構迴歸樹模型，經由模擬實驗發現，此方法在變數選取上較不會產生選取偏差，同時也具有較高的檢定力。

In past decades, many researchers have found that the ex-
haustive search method has selection bias toward variables with
more split points. In this essay, we discover that the such bias still
exists when the regression tree is applied to divide the economic
convergence clubs using some spatial or non-spatial regional data.
A new selection method is proposed by applying residual analysis
principle. We demonstrate that our selection method is relatively
unbiased and is more powerful than the commonly used one.

1. 導論

2. 模型介紹

3. 研究方法

3.1 ESM (Exhaustive Search Method)

3.2 RAM (Residual Analysis Method)

4. 模擬分析

4.1 選取偏差

4.2 檢定力

5. 實際資料分析

6. 結論

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