Studies in Industrial Statistics

NSF DMS-9704711 工业统计专题Dennis K. J. Lin 宾夕法尼亚州立大学 项目摘要 美国国家科学基金会的一个主要目标是提高 国家的智力和经济增长能力。 它通过支持新知识的发现和 提高熟练劳动力。 美国工业界越来越意识到 进行统计设计的实验。 在复杂的实验工作中，潜在因子的数量是 大，但往往只有少数几个活跃的因素。 经常遇到的一个问题是， 确定重要的因素，从而提高产品的质量和生产率。 我们希望在这里获得的基本结果将是极其 对从业者有用，因为新的设计将提供 当涉及到真实的世界问题时，这将节省相当大的成本。 当潜在因素的数量很大， 只有少数活跃因素是预期的，我们不需要无偏估计来检测这些活跃因素。 在这个项目中，我们建议使用过饱和的 设计-允许在估计中有轻微的偏差， 显著减小了实验工作中的设计尺寸。 一些初步的结果实际上被用于电子 和化学工业（特别是IBM和Lonza）。 建议的方法有助于提取有用的信息， 可能不是由“传统智慧”提供的。“从这些实际经验来看， 可以有更广泛的应用。 另一方面，本研究具有广泛的理论意义。 得到了一些理论结果，并表明这些结果可以应用于其他统计领域，如线性模型， 信息论与优化设计。 另一个统计领域已经收到了大量的 工业上的注意力涉及可靠性问题。 系统（或部件）可靠性的估计 是许多行业的共同问题。 当系统由多个组件组成时， 故障原因未指明，估计问题是 悬而未决该项目提出使用贝叶斯方法。 应用于IBM PS/2个人计算机数据的一些初步结果 已经获得。 更广泛的基础研究将 进一步研究。 这里提出的问题非常具体，需要在供资期间切实解决。 然而，它对工业和商业都有更广泛的研究影响。 应用或学术研究类型。

NSF DMS-9704711 Topics in Industrial Statistics Dennis K.J. Lin Penn State University PROJECT SUMMARY A major objective of the National Science Foundation is to improve the nation's capacity for intellectual and economic growth. It does this by supporting the discovery of new knowledge and the enhancement of a skilled workforce. American industry is becoming increasingly aware of the benefits of running statistically designed experiments. In complex experimental work, the number of potential factors is large, but often only a few active factors are expected. A problem frequently encountered is how to identify the factors that matter, and consequently improve the quality and productivity of the products. The basic results we hope to obtain here will be extremely useful to practitioners, since new designs will be furnished that will save considerable cost when real world issues are involved. When the number of potential factors is large and only a few active factors are expected, we do not need unbiased estimates to detect those active factors. In this project, we propose the use of supersaturated designs -- allowing for a slight bias in estimation but significantly reducing the design size in experimental work. Some initial results were actually utilized in electronic and chemical industries (specifically, IBM and Lonza). The proposed methodology helps extract useful information that may not be provided by "traditional wisdom." From these practical experiences, it is anticipated that a much wider application is possible. On the other hand, this research has broad theoretical implications. Some theoretical results have been obtained, and it is shown that those results can be applied to other statistical areas, such as linear models, information theory and optimal design. Another statistical area that has received a great deal of attention in industry involves reliability problems. The estimation of system (or component) reliability is a common problem for many industries. When a system consists of several components, and when the cause of failure is unspecified, the estimation problem is unresolved. This project proposes the use of a Bayesian approach. Some initial results applied to IBM PS/2 personal computer data have been obtained. More general fundamental research will be further studied. Problems given here are very specific to be realistically solved during the funding period. However, it has a much wider research impact for both industrial applications or academia type research.