摘要翻译：
统计人员被要求评估的系统，如核武器、基础设施网络、超级计算机代码和弹药，变得越来越复杂。进行完整的系统测试通常成本很高。因此，我们提出了一个方法的回顾，已提出的解决系统可靠性与有限的全系统测试。本文提出的第一种方法是结合多个信息源来评估单个部件的可靠性。第二套通用方法解决了多级数据的组合以确定系统可靠性。然后，我们通过使用贝叶斯网络和流图模型，提出了复杂系统超越传统串/并行表示的发展。我们还包括对系统可移植性评估的资源分配考虑的方法学贡献。我们用主要在洛斯阿拉莫斯国家实验室遇到的应用来说明每种方法。
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英文标题：
《Advances in Data Combination, Analysis and Collection for System
  Reliability Assessment》
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作者：
Alyson G. Wilson, Todd L. Graves, Michael S. Hamada, C. Shane Reese
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present a review of methodology that has been proposed for addressing system reliability with limited full system testing. The first approaches presented in this paper are concerned with the combination of multiple sources of information to assess the reliability of a single component. The second general set of methodology addresses the combination of multiple levels of data to determine system reliability. We then present developments for complex systems beyond traditional series/parallel representations through the use of Bayesian networks and flowgraph models. We also include methodological contributions to resource allocation considerations for system relability assessment. We illustrate each method with applications primarily encountered at Los Alamos National Laboratory.
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PDF链接：
https://arxiv.org/pdf/708.0355