摘要翻译：
在信用评级系统中，我们应用多个测试程序来验证估计的违约概率。目标是识别违约概率估计不准确的评级类别，同时仍然保持通过家庭错误率(FWER)和错误发现率(FDR)衡量的提交类型I错误的预定义级别。对于FWER，我们还考虑了采取可能的数据离散性的过程。考虑到测试统计数据。这些方法的性能在模拟设置和经验缺省数据中得到了说明。
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英文标题：
《Validation of credit default probabilities via multiple testing
  procedures》
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作者：
Sebastian D\"ohler
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最新提交年份：
2010
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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英文摘要：
  We apply multiple testing procedures to the validation of estimated default probabilities in credit rating systems. The goal is to identify rating classes for which the probability of default is estimated inaccurately, while still maintaining a predefined level of committing type I errors as measured by the familywise error rate (FWER) and the false discovery rate (FDR). For FWER, we also consider procedures that take possible discreteness of the data resp. test statistics into account. The performance of these methods is illustrated in a simulation setting and for empirical default data.
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PDF链接：
https://arxiv.org/pdf/1006.4968