Measurement Error in Nonlinear Models: A Modern Perspective
Measurement Error in Nonlinear Models: A Modern Perspective
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DOI：
10.1198/jasa.2008.s215
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发表时间：
2008-03
影响因子：
3.7
通讯作者：
D. Hall
中科院分区：
文献类型：
--
作者：
D. Hall
介绍了收集多个响应的设计分析的某些方面。因为大多数实验都有这个特点，了解这种情况的机会和挑战是从业者必不可少的阅读。第13章是一些其他专业设计的简短章节的集合，包括筛选设计、均衡设计、最优设计、空间填充设计、无趋势设计和混料设计。第14章，“把所有的东西都绑在一起”，简要讨论了在计划实验时在不同设计之间进行选择的困难任务。这一章让我更加坚信，学习实验设计的这一方面是最具挑战性的，既因为我们如何教授设计（干净地划分成整齐的块，提出的问题恰好适合一个类别），也因为可用的工具越来越广泛。本章结尾的练习帮助读者获得设计选择的经验，并提出许多发人深省的问题，即使是最老练的统计学家也会遇到挑战。这本书已经是关于这个问题的一本广泛的书，包含了丰富的信息。然而，在我的愿望清单上的其他主题将更多地讨论实验的不同作用，从探索到筛选重要因素，到表征和优化关键因素与响应之间关系的响应面方法，到接近所选最佳值的验证性实验。将设计类型与其预期目的相匹配是研究实验设计的另一个困难领域，直接讨论可以大大有助于加速这种理解。此外，提出一些可用于比较不同潜在设计的标准和图形工具也是有益的。这可以帮助形式化一个好的设计的许多方面之间的众多权衡。总的来说，现代实验设计是一个必须有参考的任何人谁将设计实验或有兴趣留在这个重要领域的前沿统计学家。我非常喜欢阅读这本写得很好、内容全面的书，既因为它对这一领域的新研究进行了仔细而清晰的综合，也因为它有许多富有洞察力的评论，有助于将方法的细节与大局联系起来。
describes some of the aspects of analysis for designs where multiple responses are collected. Because most experiments have this feature, understanding the opportunities and challenges for this situation is essential reading for practitioners. Chapter 13 is a collection of short sections on a number of other specialized designs, including screening, equileverage, optimal, space-filling, trend-free, and mixture designs. Chapter 14, “Tying It All Together,” briefly discusses the difficult task of choosing between different designs when planning an experiment. This chapter reinforces my belief that learning this aspect of design of experiments is most challenging, both because of how we teach design (cleanly compartmentalized into tidy chunks with questions posed to fit precisely into a category) and because of the ever-increasing breadth of tools available. The exercises at the conclusion of this chapter help the reader gain experience with design selection and pose many thought-provoking questions that will challenge even the most seasoned statistician. Already an extensive volume on the subject, this book contains a wealth of information. However, on my wish list for additional topics would be more discussion about the different roles of experimentation, from exploration, to screening for important factors, to response surface methods for characterizing and optimizing the relationship between the key factors and the response, to confirmatory experiments near the chosen optimum. Matching types of designs to their intended purposes is another area that is difficult for those studying design of experiment, and direct discussion can greatly help accelerate this understanding. In addition, it would be beneficial to present some of the criteria and graphical tools that are available to compare different potential designs. This could help formalize the numerous trade-offs between the many aspects of a good design. Overall, Modern Experimental Design is a must-have reference for anyone who will be designing experiments or for statisticians interested in remaining on the leading edge of this important area. I thoroughly enjoyed reading this well-written and comprehensive book, both for the careful and clear synthesis of the new research in this area and for the many insightful comments that help connect the details of the methods to the big picture.