Robust discrimination designs
Robust discrimination designs
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稳健的区分设计
DOI：
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发表时间：
2009
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影响因子：
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通讯作者：
D. Wiens
中科院分区：
文献类型：
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作者：
D. Wiens
概括。 我们研究实验设计的构建，其目的是帮助区分两个可能的非线性回归模型，每个模型可能只是近似指定的。我们的方法的粗略描述是，我们对每个回归响应施加邻域结构，并确定这些邻域中在最小化 Kullback-Leibler 散度方面最不利的成员。获得最大化该最小发散的设计。研究了静态方法和顺序方法。然后，我们考虑顺序设计，其最初目的是区分，但随着一种模型比另一种模型更受青睐，其重点转向有效估计或预测。
Summary. We study the construction of experimental designs, the purpose of which is to aid in the discrimination between two possibly non‐linear regression models, each of which might be only approximately specified. A rough description of our approach is that we impose neighbourhood structures on each regression response and determine the members of these neighbourhoods which are least favourable in the sense of minimizing the Kullback–Leibler divergence. Designs are obtained which maximize this minimum divergence. Both static and sequential approaches are studied. We then consider sequential designs whose purpose is initially to discriminate, but which move their emphasis towards efficient estimation or prediction as one model becomes favoured over the other.