The Elements of Statistical Learning
The Elements of Statistical Learning
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DOI：
10.1198/tech.2003.s770
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发表时间：
2003-08
期刊：
影响因子：
2.5
通讯作者：
E. Ziegel
中科院分区：
文献类型：
--
作者：
E. Ziegel
第11章包括更多其他领域的案例研究，从制造业到市场研究。第12章以一些关于MTS的科学贡献的评论结束了这本书。田口实验设计方法在过去的几十年里在统计界引起了相当大的争议。MTS/MTGS方法似乎是关于其所倡导的方法论的另一个讨论来源（蒙哥马利，2003）。正如Woodall等人（2003年）所指出的，MTS/MTGS方法被认为是特设的，因为它们没有使用任何基本的统计理论来开发。由于“正常”和“异常”群体构成了理论的基础，因此一些抽样限制是应用的基础。首先，“正常”样本必须是均匀的、无偏的和/或完整的，以便获得可靠的测量尺度。其次，当使用OAs时，“异常”样本的选择是降维成功的关键。例如，如果每个异常项在医学示例中确实是唯一的，则当使用越大越好类型的S/N比时，不清楚如何能够保证统计距离MD在连续尺度上给出一致的严重性诊断度量。多元诊断对Technometrics读者来说并不陌生，现在在统计分析和数据挖掘知识发现中越来越受欢迎。作为一个有前途的替代方案，假设没有底层的数据模型，马哈拉诺比斯-田口策略并没有提供足够的证据证明使用所提出的方法比现有的工具所取得的收益。读者可能对与其他诊断工具（如逻辑回归和基于树的方法）的详细比较非常感兴趣。总的来说，虽然MTS/MTGS的想法很有趣，但如果这本书是作为技术参考以严格的方式写的，它会更有价值。甚至在一些数学符号中也有一些不精确的地方。也许后续的额外理论论证和仔细的案例研究将回答一些挥之不去的问题。
Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research. Chapter 12 concludes the book with some commentary about the scienti c contributions of MTS. The Taguchi method for design of experiment has generated considerable controversy in the statistical community over the past few decades. The MTS/MTGS method seems to lead another source of discussions on the methodology it advocates (Montgomery 2003). As pointed out by Woodall et al. (2003), the MTS/MTGS methods are considered ad hoc in the sense that they have not been developed using any underlying statistical theory. Because the “normal” and “abnormal” groups form the basis of the theory, some sampling restrictions are fundamental to the applications. First, it is essential that the “normal” sample be uniform, unbiased, and/or complete so that a reliable measurement scale is obtained. Second, the selection of “abnormal” samples is crucial to the success of dimensionality reduction when OAs are used. For example, if each abnormal item is really unique in the medical example, then it is unclear how the statistical distance MD can be guaranteed to give a consistent diagnosis measure of severity on a continuous scale when the larger-the-better type S/N ratio is used. Multivariate diagnosis is not new to Technometrics readers and is now becoming increasingly more popular in statistical analysis and data mining for knowledge discovery. As a promising alternative that assumes no underlying data model, The Mahalanobis–Taguchi Strategy does not provide suf cient evidence of gains achieved by using the proposed method over existing tools. Readers may be very interested in a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods. Overall, although the idea of MTS/MTGS is intriguing, this book would be more valuable had it been written in a rigorous fashion as a technical reference. There is some lack of precision even in several mathematical notations. Perhaps a follow-up with additional theoretical justi cation and careful case studies would answer some of the lingering questions.