比较分布指的是包含传统拟合优度测试的统计数据分析。后者只包括单样本和K样本问题的正式统计假设检验，而本书通过考虑图形和估计方法，提供了更一般和信息更丰富的处理方法。当一个程序提供拒绝无效假设的原因的信息时，它被称为信息性的。尽管方法的发展在历史上看似不同，但本书通过将它们与共同的理论支柱联系起来，强调了方法之间的相似性。
这本书由两部分组成。第一部分讨论了单样本问题的统计方法。本书的第二部分讨论了K样本问题。这本书第二部分的许多章节可能会引起每一位从事比较研究的统计学家的兴趣。
这本书对各种拟合优度方法进行了独立的理论处理，包括图解法、假设检验、模型选择和密度估计。它依赖于保持在中间水平的参数、半参数和非参数理论；通常还会提供这些方法背后的直觉和启发。这本书包含了许多数据示例，这些示例是用作者编写的cd-R-package进行分析的。所有的例子都包括R代码。
因为这本书中描述的许多方法属于几乎所有统计学家的基本工具箱，所以这本书应该引起广大读者的兴趣。特别是，这本书可能对需要在拟合优度测试领域进行研究的研究人员、研究生和博士生有用。从业人员和应用统计学家也可能感兴趣，因为有许多例子、R代码和强调程序的信息性。
奥利维尔·塔斯是根特大学生物统计学副教授。他发表了关于拟合优度测试的方法论论文，但他也在环境统计和基因组学领域发表了更多的应用性工作。
Comparing Distributions
Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing. Whereas the latter includes only formal statistical hypothesis tests for the one-sample and the K-sample problems, this book presents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the null hypothesis. Despite the historically seemingly different development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone.
This book consists of two parts. In the first part statistical methods for the one-sample problem are discussed. The second part of the book treats the K-sample problem. Many sections of this second part of the book may be of interest to every statistician who is involved in comparative studies.
The book gives a self-contained theoretical treatment of a wide range of goodness-of-fit methods, including graphical methods, hypothesis tests, model selection and density estimation. It relies on parametric, semiparametric and nonparametric theory, which is kept at an intermediate level; the intuition and heuristics behind the methods are usually provided as well. The book contains many data examples that are analysed with the cd R-package that is written by the author. All examples include the R-code.
Because many methods described in this book belong to the basic toolbox of almost every statistician, the book should be of interest to a wide audience. In particular, the book may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied statisticians may also be interested because of the many examples, the R-code and the stress on the informative nature of the procedures.
Olivier Thas is Associate Professor of Biostatistics at Ghent University. He has published methodological papers on goodness-of-fit testing, but he has also published more applied work in the areas of environmental statistics and genomics.
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