Flexible Imputation of Missing Data, 2nd ed.: Boca Raton, FL: Chapman & Hall/CRC Press, 2018, xxvii + 415 pp., $91.95(H), ISBN: 978-1-13-858831-8.
Flexible Imputation of Missing Data, 2nd ed.: Boca Raton, FL: Chapman & Hall/CRC Press, 2018, xxvii + 415 pp., $91.95(H), ISBN: 978-1-13-858831-8.
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缺失数据的灵活插补，第二版：博卡拉顿，佛罗里达州：查普曼
DOI：
10.1080/01621459.2019.1662249
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发表时间：
2019
影响因子：
3.7
通讯作者：
Yang, Shu
中科院分区：
文献类型：
--
作者：
Yang, Shu
在实际应用中，经常会遇到数据缺失的情况。一类更广泛的缺失数据被称为不完全数据，它包括具有测量误差的数据、具有潜在变量的多水平数据以及因果推理中的潜在结果。由于其直观的吸引力，多重填充一直是处理不完整数据的最流行的方法：它倍增填充缺失的值，并基于简单的规则汇集分析。本书主要关注对不完整数据的多重填充方法，而不是替代方法，如加权过程和基于似然的方法。这本书的主要目的是为从业者提供一个执行多重归责的工具包。这本书的第一版(Van Buuren 2012)在统计和应用研究界很受欢迎和好评。正文解释了多重归责的基本和关键思想，如何在实践中实施，以及如何报告和解释结果。此外，它使用了方便读者的风格，并以MICE包为基础，提供了大量的例证。因此，这本书也可以被视为扩展的MICE教程。根据作者自己的工作和该领域的最新发展，新版扩大了对重要主题的讨论或纳入了新的主题。这个版本的特色是关于多水平数据的多重归因的新章节，通过潜在结果的多重归因的因果推理，以及用于归算的新的数据自适应算法，如预测均值匹配、树和许多其他机器学习工具。我最喜欢这本书的一个方面是它在实用性和技术性之间取得了很好的平衡。一方面，它避免了太多的数学和技术细节，并使用图形工具和可视化显示来帮助理解。从业者能够理解大局并遵循书中提供的代码。另一方面，理论家能够找到技术材料和期刊文章的参考文献进行深入研究。这本书分为三个部分，首先介绍了有用的背景材料和概述。第二章回顾了多重归责的历史沿革，介绍了多重归责的主要内容。
Missing data are frequently encountered in practice. A broader class of missing data is called incomplete data, which includes data with measurement error, multilevel data with latent variables, and potential outcomes in causal inference. Due to its intuitive appeal, multiple imputation has been the most popular method for handling incomplete data: it multiply fills in the missing values and pools analyses based on straightforward rules.The book focuses mainly on the multiple imputation approach to incomplete data but not on alternative approaches, such as weighting procedures and likelihood-based approaches. The main objective of the book is to provide a tool kit for practitioners to execute multiple imputation. The first edition of this book (van Buuren 2012) has been popular and wellreceived in the statistics and applied research communities. The main text explains the basic and key ideas underpinning multiple imputation, how to implement it in practice, and how to report and interpret the results. Moreover, it uses a readerfriendly style with lots of worked-out examples for illustration based on the MICE package. Therefore, the book can also be viewed as an extended MICE tutorial. Drawing from the author’s own work and from the most recent developments in the field, the new edition expands discussions on important topics or incorporates new topics. This edition features new chapters on multiple imputation of multilevel data, causal inference by multiple imputation of the potential outcomes, and new dataadaptive algorithms for imputation such as predictive mean matching, trees, and many other machine learning tools. The aspect I like most about this book is that it is well-balanced between practicality and technicality. On the one hand, it avoids too much mathematical and technical details and uses graphical tools and visual displays to aid understanding. Practitioners are able to understand the big picture and follow the code provided in the book. On the other hand, theoreticians are able to find technical materials and references to journal articles for in-depth investigation. Divided into three parts, this book begins by an introduction of useful background material and an overview. Chapter 2 reviews the history of multiple imputation and introduces