Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
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DOI：
10.1198/tech.2005.s836
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发表时间：
2005-02
期刊：
影响因子：
2.5
通讯作者：
D. Zelterman
中科院分区：
文献类型：
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作者：
D. Zelterman
本书由长度各异的八章组成。在主要章节末尾添加一些章节来收集更多技术论点并提供参考书目注释效果很好，可以帮助读者更深入地探索他们感兴趣的 RSS 方面。第一章介绍RSS的概念和一般流程。这一非常有用的章节将使读者能够快速进入 RSS 领域，了解其历史发展，并确定特别感兴趣的应用程序。第 2 章和第 3 章讨论平衡 RSS。特别是，第 2 章重点关注非参数 RSS，其中没有对感兴趣变量的基本分布做出假设。本章详细研究了 RSS 相对于 SRS 在总体均值、均值平滑函数和总体分位数估计方面的相对效率。作者还考虑了推理程序，例如置信区间的构建和假设检验。为了便于基于RSS样本分位数的推理过程，他们还讨论了密度估计的核方法。这一段很有趣。本章还介绍了一些基于 RSS 数据的 M 估计的稳健程序。第 3 章讨论参数 RSS，其中假设感兴趣变量的基本分布属于某个参数分布族（例如，位置尺度族和形状尺度族）。作者通过 Fisher 信息很好地为参数化 RSS 奠定了理论基础。研究了基于RSS的最大似然估计(MLE)及其相对于基于SRS的MLE的相对效率，并讨论了位置分布族的最佳线性无偏估计。第 4 章研究不平衡 RSS。本章首先开发了分析 RSS 数据以推断分布函数和分位数以及一般统计函数的方法。详细讨论了参数位置尺度族和分位数非参数估计的优化设计。本章还包含贝叶斯设计和自适应设计的方法。第 5 章探讨了 RSS 背景下的经典无分发测试。作者考虑了符号检验、符号秩检验和 Mann-Whitney-Wilcoxon 检验，并重新审视了无分布检验的最优设计问题。具有 Gibbons 和 Chakraborti (2003) 水平的非参数检验先验知识的读者会发现本章内容丰富且易于理解。对于不熟悉这些标准主题的读者来说，一些简短的附加解释和参考可能有助于更广泛的访问。第 6 章描述了带有伴随变量的 RSS。开发了多层RSS方案和使用多个伴随变量的自适应RSS方案；讨论了使用 RSS 的一般回归分析；并探讨了基于伴随变量的回归分析的最佳 RSS 方案的设计。第 7 章阐述了 RSS 作为数据挖掘的数据缩减工具，而第 8 章则通过案例研究举例说明了 RSS 的实用功能。最后一章包含有关 RSS 案例研究的内容，进一步增强了本专着对于从业者和应用统计学家的价值。在RSS的开发中，排序集大小k和循环数m的选择直接关系到实际问题。如果添加一些关于选择的更详细的讨论，本书将会更有用。然而，总的来说，我强烈向研究人员和从业者推荐这本写得很好且价格合理的书，他们所有人都可能使用它所讨论的一种或多种方法。
The book comprises eight chapters of varying length. The inclusion of sections at the end of the main chapters to collect more technical arguments and to give bibliographic notes works well and helps the readers explore in more depth aspects of RSS in which they are interested. Chapter 1 introduces the notion and general procedure of RSS. This very useful chapter will enable readers to quickly enter into the realm of RSS, learn about its historical developments, and identify applications of particular interest. Chapters 2 and 3 discuss balanced RSS. In particular, Chapter 2 focuses on nonparametric RSS, in which no assumption on the underlying distribution of the variable of interest is made. This chapter studies in detail the relative efficiency of RSS with respect to SRS in the estimation of a population mean, a smooth function of means, and population quantiles. The authors also consider the inference procedures, such as the construction of confidence intervals and hypothesis testing. To facilitate the inference procedures based on RSS sample quantiles, they also discuss the kernel method of density estimation. This section is quite interesting. The chapter also presents some robust procedure based on M-estimates with RSS data. Chapter 3 addresses parametric RSS, where the underlying distribution of the variable of interest is assumed to belong to some parametric family (e.g., location-scale family and shape-scale family) of distributions. The authors nicely lay out the theoretical foundation for the parametric RSS via Fisher information. The maximum likelihood estimate (MLE) based on RSS and its relative efficiency with respect to MLE based on SRS are studied, and the best linear unbiased estimate for location family of distributions is dealt with. Chapter 4 studies unbalanced RSS. This chapter first develops the methodology of analyzing RSS data for the inferences on distribution functions and quantiles, as well as general statistical functionals. The optimal designs for the parametric location-scale family and for nonparametric estimation of quantiles are discussed in detail. This chapter also contains methods of Bayes design and adaptive design. Chapter 5 explores classical distribution-free tests in the context of RSS. The authors consider the sign test, signed rank test, and Mann–Whitney– Wilcoxon tests and revisit the issue of the optimal design for distribution-free tests. Readers with a prior knowledge of nonparametric tests at the level of Gibbons and Chakraborti (2003) will find this chapter informative and easy to understand. For readers not familiar with these standard topics, some brief additional explanation and references might have been beneficial for the wider accessibility. Chapter 6 describes RSS with concomitant variables. A multilayer RSS scheme and an adaptive RSS scheme using multiple concomitant variables are developed; the general regression analysis using RSS is discussed; and the design of optimal RSS schemes for regression analysis, on the basis of the concomitant variables, is explored. Chapter 7 illustrates RSS as a data reduction tool for data mining, whereas Chapter 8 exemplifies the practical features of RSS via case studies. The inclusion of this last chapter on case studies with RSS further enhances the value of this monograph for practitioners and applied statisticians. In the development of RSS, the choices of ranked set size k and cycle number m are directly pertinent to practical problems. This book would have been more useful had some more detailed discussion on the choices been added. However, overall I would highly recommend this well-written and reasonably priced book to researchers and practitioners, all of whom are likely to use one or more of the methods it discusses.