验证和认证的假设检验
Hypothesis Testing for Validation and Certification
摘要：
我们开发了一个假设检验的框架，制定的问题1）验证的仿真模型和2）使用建模来证明物理系统的性能。这些结果用于解决外推验证和认证问题，即感兴趣的制度与我们有实验数据的制度不同的问题。我们使用测量集中理论来开发测试并分析它们的误差。这项工作受到Lucas，Owhadi和Ortiz的工作的刺激，其中描述和测试了严格的验证和认证方法。在一个评论中，我们描述了这两种方法之间的联系。此外，正如在这项工作中提到的，这些结果在边际和非边际量化（QMU）框架中具有重要意义。特别是，在一句话中，我们描述了它如何提供了一个严格的解释的概念的信心和新的概念的利润率和不确定性，允许这种解释。由于上述测试中使用的某些浓度参数可能是未知的，我们还显示，在后半部分的文件，如何获得同样强大的测试，估计他们从样本数据，从而取代假设的浓度参数的值较弱的假设。这篇论文基本上是2010年4月10日一篇论文的精确副本。
英文摘要：
We develop a hypothesis testing framework for the formulation of the problems of 1) the validation of a simulation model and 2) using modeling to certify the performance of a physical system. These results are used to solve the extrapolative validation and certification problems, namely problems where the regime of interest is different than the regime for which we have experimental data. We use concentration of measure theory to develop the tests and analyze their errors. This work was stimulated by the work of Lucas, Owhadi, and Ortiz where a rigorous method of validation and certification is described and tested. In a remark we describe the connection between the two approaches. Moreover, as mentioned in that work these results have important implications in the Quantification of Margins and Uncertainties (QMU) framework. In particular, in a remark we describe how it provides a rigorous interpretation of the notion of confidence and new notions of margins and uncertainties which allow this interpretation. Since certain concentration parameters used in the above tests may be unkown, we furthermore show, in the last half of the paper, how to derive equally powerful tests which estimate them from sample data, thus replacing the assumption of the values of the concentration parameters with weaker assumptions. This paper is an essentially exact copy of one dated April 10, 2010.
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