Gaussian Markov Random Fields: Theory and Applications
Gaussian Markov Random Fields: Theory and Applications
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DOI：
10.1198/tech.2006.s352
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发表时间：
2006-02
期刊：
影响因子：
2.5
通讯作者：
J. Picka
中科院分区：
文献类型：
--
作者：
J. Picka
无相互作用的分类”）和13（“有相互作用的双向交叉分类”）。每一章都包含两个或两个以上的数值例子，除了第14章（“三向和高阶交叉分类”）和第17章（“一般r向嵌套分类”），这两章只包含一个例子。示例出现在估计、置信区间和假设检验部分。估计量的分布仅在第11章和第15章（“双向嵌套分类”）中讨论。第11、13、15和16章（“三向嵌套分类”）包含了涉及不平衡实验的设计考虑的信息。附录中包含的基本理论和方法的结果有用的发展不平衡的随机模型，以及信息的能力，广泛使用的软件。讨论的软件包有SAS、SPSS、BMDP、S-PLUS、GENSTAT和BUGS。这本书组织得很好，重点很突出。它包含交叉和嵌套的不平衡模型的广泛覆盖。由于主题的数量，报道的深度有时是有限的。这只是一个小问题，因为总是有大量的参考文献。书的组织和材料的呈现使困难的主题更容易理解。这本书的主要缺点是它只涉及完全随机的单变量模型。考虑到书中的信息量，这是可以理解的。作者在前言中指出了这一缺点，并建议将来可能会有一项涵盖这些主题的工作。对于面向应用的从业者来说，一个小缺点是，所讨论的一些估计方法虽然有趣，但在更常用的统计软件包中找不到。无论如何，这本书对于任何使用不平衡随机模型的人来说都是一个很好的资源。
Classification Without Interaction”), and 13 (“Two-Way Crossed Classification With Interaction”). Every chapter contains two or more numerical example with the exception of Chapters 14 (“Three-Way and Higher-Order Crossed Classifications”) and 17 (“General r-Way Nested Classification”), which only contain one example each. Examples appear in the estimation, confidence interval, and hypothesis testing sections. Distribution of estimators is only discussed for the models in Chapters 11 and 15 (“Two-Way Nested Classification”). Chapters 11, 13, 15, and 16 (“Three-Way Nested Classification”) contain information on design considerations involving unbalanced experiments. The appendixes contain basic theoretical and methodological results useful in the development of unbalanced random models as well as information on the capabilities of widely available software. Packages discussed are SAS, SPSS, BMDP, S–PLUS, GENSTAT, and BUGS. The book is well organized and focused. It contains extensive coverage on crossed and nested unbalanced models. Because of the number of topics, the depth of coverage is occasionally limited. This is only a minor issue, since there are always a substantial number of references given. The organization of the book and the presentation of the material make difficult subject matter easier to follow. The main drawback to the book is that it deals only with completely random univariate models. Given the volume of information in the book, however, this is understandable. The authors point out this shortcoming in the Preface and suggest that a future work covering these topics may be forthcoming. For the application-oriented practitioner, a small disadvantage is that a number of the estimation approaches discussed, while interesting, cannot be found in the more commonly used statistical software packages. Regardless, the book makes an excellent resource for anyone working with unbalanced random models.