對於右設限資料及不等變異性迴歸轉換模型，Chen 和 Chang (1999)在最近提出二種估計方法──離散方法和篩子方法，在他們的論文中，只針對誤差項是標準極值分配及兩樣本資料，與ROC (Hsieh, 1996)法及最大偏概似法(MPLE)做比較，在我們論文中，我們想要比較更多的估計方法，同時，考慮誤差項具標準邏輯分配的情況，藉由這些更廣泛的模擬比較，我們探討在迴歸轉換模型情形下，使用離散方法和篩子方法的優點。

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Recently Chen and Chang (1999) proposed two approaches, the discrete and sieve methods, for regression-transformation model with heteroscedastic and right censored data. In their paper, they only compare with two methods under situation that the error term is standard extreme value distributed and there is two sample data. In our paper, we would like to compare these two approaches with more approaches, and simultaneously we also consider the situation that the error term is standard logistic distributed. By these more extensive simulative comparisons, we research the advantage of using these two methods when regression-transformation models are appropriate.
2 Estimation methods

2.1 The estimation method of Cheng et al. (1995)

2.2 The maximum partial likelihood method

2.3 The estimation method of Dabrowska & Doksum (1988a)

2.4 The ROC method

2.5 The estimation methods of Chen and Chang (1999)

2.5.1 The discrete method

2.5.2 The sieve method

3 Simulations on two-sample case

3.1 σ-proportional hazard model

3.1.1 γ is known and equal to zero

3.1.2 γ is unknown

3.2 σ-proportional odds model

3.2.1 γ is known and equal to zero

3.2.2 γ is unknown

4 Simulations on one-sample case

4.1 σ-proportional hazard model

4.1.1 γ is known and equal to zero

4.1.2 γ is unknown

4.2 σ-proportional odds model

4.2.1 γ is known and equal to zero

4.2.2 γ is unknown

5 Discussion

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