此篇論文所討論的問題是關於尋找一最適設計，其具有模型區分的作用，並同時具有模型穩健的性質。在此篇論文當中，模型穩健的想法是在於假設之模型與真實之模型間，最大的模型偏差可以被最小化。其中，關於模型區分部分的標準是基於 Atkinson 與 Fedorov (1975) 所提出的T-最適設計標準，滿足此一標準的設計能使得在於設計點上的模型誤差平方和達到最大。另外，關於模型穩健設計部分的標準，採用的則是使得兩競爭的模型間，最大的差距最小化的標準。在此篇文章中，我們對於一些常用的二元反應模型，例如 Probit 或者是 Logit模型等，找出了滿足上述兩個標準的最適設計。

This paper is concerned with the problem of finding an experimental design for discrimination between two rival models and for model robustness that minimizing the maximum bias simultaneously in binary response experiments. The criterion for model discrimination is based on the $T$-optimality criterion proposed in Atkinson and Fedorov (1975), which maximizes the sum of squares of deviations between the two rival models while the criterion for model robustness is based on minimizing the maximum probability bias of the two rival models. In this paper we obtain the optimum designs satisfy the above two criteria for some commonly used rival models in binary response experiments such as the probit and logit models etc.

Introduction
Desired TLS-optimum design
Efficiency and bias comparisons
Discussions and conclusions

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