Studies in Industrial Statistics

国家科学基金会DMS-9704711工业统计专题丹尼斯·K·J·林宾夕法尼亚州立大学项目摘要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加强熟练劳动力来做到这一点。美国工业界越来越意识到进行统计设计实验的好处。在复杂的实验工作中，潜在因素的数量是很多的，但往往只有几个积极的因素是被期待的。一个经常遇到的问题是如何确定重要的因素，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对从业者非常有用，因为当涉及到现实世界的问题时，将提供新的设计，将节省相当大的成本。当潜在因素的数量很多，并且只有几个积极因素被预期时，我们不需要无偏估计来检测这些积极因素。在这个项目中，我们建议使用过饱和设计--允许在估计中略有偏差，但在实验工作中显著减少设计规模。一些初步成果实际用于电子和化工行业(具体地说，是IBM和龙沙)。建议的方法有助于提取“传统智慧”可能无法提供的有用信息。从这些实际经验中，可以预见到更广泛的应用是可能的。另一方面，本研究具有广泛的理论意义。这些结果可应用于线性模型、信息论和优化设计等其他统计领域。另一个在工业界受到极大关注的统计领域涉及可靠性问题。系统(或部件)可靠性评估是许多行业普遍存在的问题。当一个系统由多个组件组成，并且故障原因不明时，估计问题就无法解决。该项目建议使用贝叶斯方法。对IBM PS/2个人计算机数据进行了处理，得到了一些初步的结果。更一般的基础研究将进一步研究。这里给出的问题是非常具体的，需要在供资期间切实解决。然而，无论是对工业应用还是学术类型的研究，它都有更广泛的研究影响。

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.