Models for Repeated Measurements
Models for Repeated Measurements
复制标题
重复测量模型
作者：
F. Kianifard
本书简要介绍了SAS、SPSS和BMDP，以及它们在进行方差分析时的用法。这本书还有一章专门介绍实验设计和相应的方差分析。在覆盖面方面，这本书的一个很好的特点是包括了一个关于夜间人口模型的章节--通常在关于实验设计和方差分析的书中找不到。这本书的最后给出了几个附录，讨论了一些标准分布、萨特斯韦特近似、平方和的计算规则、自由度、期望均方等等。每章结尾处的练习都包含一些数值问题。我对这本书的一些吹毛求疵如下。有时，它只是在没有足够的动机或例子的情况下给出表达。不熟悉方差分析技术的读者会想知道其中一些表达的相关性。仅举个例子，第65页上有数量“对比平方和”。然后说明了由于具有ƒ1 df的效果而产生的一组ƒ1正交对比的平方和将等于平方和的代数性质。考虑到这本书的水平，关于这样一个性质的讨论似乎无关紧要。我在这本书的任何地方都没有看到这个属性；我也没有看到由于本书后面明确使用或提到的对比而产生的平方和。只有在介绍了模型和假设之后，才会提到单向模型足够的例子，并且这些例子被隐藏在模型后面的备注中(用小号字体)。具有交互作用的双向模型也是如此(第4)。两位作者在序言中指出，这些评论主要是为了包括不在正文之外的结果。我认为好的例子应该是引入方差分析模型的起点。作者同时给出了X模型、随机模型和混合模型的分析。区分这些情景的振奋人心的例子本应成为每一章介绍的重点，而不是推迟到本章“已制定的例子”下的后面部分或隐藏在评论中。作者讨论了纠正正态和同方差缺失的变换(SEC。2.22)。然而，这些都没有用任何真实的例子来说明。关于偏离模型假设的检验，形式检验有较详细的介绍；然而，图形程序只是在备注中非常简短地提到。我认为这是一个明显的遗漏。因此，我不太愿意向任何对使用方差分析进行实际数据分析感兴趣的人推荐这本书，除非应用程序是这样的：已知其中一个标准模型(以及标准假设)是足够的，并且不需要进行诊断检查。显然，在大多数应用程序中，这不太可能出现。抛开前面的批评不谈，我可以看到我自己查阅这本书是为了引用ANOVA表，查找随机或混合效应模型下的期望值或测试统计，或者引用SAS、SPSS或BMDP来执行ANOVA。这本书确实是基于xx、随机和混合效应模型的方差分析的极好参考来源。
the book provides a brief introduction to SAS, SPSS, and BMDP, along with their use in performing ANOVA. The book also has a chapter devoted to experimental designs and the corresponding ANOVA. In terms of coverage, a nice feature of the book is the inclusion of a chapter on nite population models—typically not found in books on experimental designs and ANOVA. Several appendixes are given at the end of the book discussing some of the standard distributions, the Satterthwaite approximation, rules for computing the sums of squares, degrees of freedom, expected mean squares, and so forth. The exercises at the end of each chapter contain a number of numerical problems. Some of my quibbles about the book are the following. At times, it simply gives expressions without adequate motivation or examples. A reader who is not already familiar with ANOVA techniques will wonder as to the relevance of some of the expressions. Just to give an example, the quantity “sum of squares due to a contrast” is de ned on page 65. The algebraic property that the sums of squares due to a set of a ƒ 1 orthogonal contrasts will add up to the sum of squares due to an effect having a ƒ 1 df is then stated. Given the level of the book, discussion of such a property appears to be irrelevant. I did not see this property used anywhere in the book; neither did I see the sum of squares due to a contrast explicitly used or mentioned later in the book. Examples in which the one-way model is adequate are mentioned only after introducing the model and the assumptions, and the examples are buried inside the remarks (in small print) following the model. This is also the case with the two-way model with interaction (Chap. 4). The authors indicate in the preface that the remarks are mostly meant to include results to be kept out of the main body of the text. I believe that good examples should be the starting point for introducing ANOVA models. The authors present the analysis of xed, random, and mixed models simultaneously. Motivating examples that distinguish between these scenarios should have been made the highlight of the presentation in each chapter rather than deferred to the later part of the chapter under “worked out examples” or buried within the remarks. The authors discuss transformations to correct lack of normality and lack of homoscedasticity (Sec. 2.22). However, these are not illustrated with any real examples. Regarding tests concerning the departure from the model assumptions, formal tests are presented in some detail; however, graphical procedures are only very brie y mentioned under a remark. I consider this to be a glaring omission. Consequently, I would be somewhat hesitant to recommend this book to anyone interested in actual data analysis using ANOVA unless the application is such that one of the standard models (along with the standard assumptions) is known to be adequate and diagnostic checks are not called for. Obviously, this is an unlikely scenario in most applications. The preceding criticisms aside, I can see myself consulting this book to refer to an ANOVA table, to look up an expected value or test statistic under a random or mixed-effects model, or to refer to the use of SAS, SPSS, or BMDP for performing ANOVA. The book is indeed an excellent source of reference for the ANOVA based on xed, random, and mixed-effects models.