这是什么 啊 ，版主解释一下啊

混合模型<br/><br/>看review:<br/>SAS for Mixed Models, Second Edition addresses the large class of
statistical models with random and fixed effects. Mixed models occur
across most areas of inquiry, including all designed experiments, for
example. This book should be required reading for all statisticians,
and will be extremely useful to scientists involved with data analysis.
Most pages contain example output, with the capabilities of mixed
models and SAS software clearly explained throughout. I have used the
first edition of SAS for Mixed Models as a textbook for a second-year
graduate-level course in linear models, and it has been well received
by students. The second edition provides dramatic enhancement of all
topics, including coverage of the new GLIMMIX and NLMIXED procedures,
and a chapter devoted to power calculations for mixed models. The
chapter of case studies will be interesting reading, as we watch the
experts extract information from complex experimental data (including a
microarray example). I look forward to using this superb compilation as
a textbook. --Arnold Saxton, Department of Animal Science, University
of Tennessee<br/>

<p>【书名】 SAS for Mixed Models, Second Edition<br/>【作者】Ramon C. Littell, Ph.D. , George A Milliken, Ph.D., Walter W. Stroup, Ph.D., Russell D. Wolfinger, Ph.D., Oliver </p><p>Schabenberger, Ph.D.<br/>【出版社】2006, SAS Institute Inc., Cary, NC, USA<br/>【版本】Second Edition<br/>【出版日期】1st printing, February 2006<br/>【文件格式】PDF，<br/>【文件大小】压缩文件4.89,解压后8.31<br/>【页数】算封面共834页<br/>【ISBN出版号】ISBN-13: 978-1-59047-500-3；ISBN-10: 1-59047-500-3<br/>【资料类别】统计学，mixed models教程<br/>【扫描版还是影印版】超清晰影印版<br/>【是否缺页】全，不缺页<br/>【关键词】Mixed medels；Generalized Linear Mixed Models(GLMM)；Linear Mixed Model(LMM)<br/>【内容简介】<br/>&nbsp; The indispensable, up-to-date guide to mixed models using SAS. Discover the latest capabilities available for a variety of </p><p>applications featuring the MIXED, GLIMMIX, and NLMIXED procedures in this valuable edition of the comprehensive mixed models </p><p>guide for data analysis, completely revised and updated for SAS9. The theory underlying the models, the forms of the models </p><p>for various applications, and a wealth of examples from different fields of study are integrated in the discussions of these </p><p>models:<br/>&nbsp;&nbsp;&nbsp; * random effect only and random coefficients models</p><p>&nbsp;&nbsp;&nbsp; * split-plot, multilocation, and repeated measures models</p><p>&nbsp;&nbsp;&nbsp; * hierarchical models with nested random effects</p><p>&nbsp;&nbsp;&nbsp; * analysis of covariance models</p><p>&nbsp;&nbsp;&nbsp; * spatial correlation models</p><p>&nbsp;&nbsp;&nbsp; * generalized linear mixed models</p><p>【目录】 <br/>Contents<br/>Preface ix<br/>Chapter 1 Introduction </p><p>Chapter 2 Randomized Block Designs ……17</p><p>Chapter 3 Random Effects Models…… 57</p><p>Chapter 4 Multi-factor Treatment Designs with Multiple Error Terms…… 93</p><p>Chapter 5 Analysis of Repeated Measures Data ……159</p><p>Chapter 6 Best Linear Unbiased Prediction ……205</p><p>Chapter 7 Analysis of Covariance ……243</p><p>Chapter 8 Random Coefficient Models…… 317</p><p>Chapter 9 Heterogeneous Variance Models ……343</p><p>Chapter 10 Mixed Model Diagnostics ……413</p><p>Chapter 11 Spatial Variability…… 437</p><p>Chapter 12 Power Calculations for Mixed Models…… 479</p><p>Chapter 13 Some Bayesian Approaches to Mixed Models……497</p><p>Chapter 14 Generalized Linear Mixed Models ……525</p><p>Chapter 15 Nonlinear Mixed Models ……567</p><p>Chapter 16 Case Studies …… 637</p><p>Appendix 1 Linear Mixed Model Theory ……733</p><p>Appendix 2 Data Sets…… 757</p><p>References ……781</p><p>Index ……795<br/>【整理书评】</p><p>“It may appear that for each of the main categories (linear, generalized linear, and nonlinear<br/>mixed models) there is one and only one SAS procedure available (MIXED, GLIMMIX, and<br/>NLMIXED, respectively), but the reader should be aware that this is a rough rule of thumb<br/>only. There are situations where fitting a particular model is easier in a procedure other than the<br/>one that seems the obvious choice. For example, when one wants to fit a mixed model to binary<br/>data, and one insists on using quadrature methods rather than quasi-likelihood, NLMIXED is<br/>the choice.”<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Geert Verbeke<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Biostatistical Centre, Katholieke Universiteit Leuven, Belgium<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Geert Molenberghs<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Center for Statistics, Hasselt University, Diepenbeek, Belgium</p><p>“The new edition illustrates how to compute statistical power for many experimental<br/>designs, using tools that are not available with most other software, because of this book’s<br/>foundation in mixed models. Chapters discussing the relatively new GLIMMIX and NLMIXED<br/>procedures for generalized linear mixed model and nonlinear mixed model analyses will prove<br/>to be particularly profitable to the user requiring assistance with mixed model inference for<br/>cases involving discrete data, nonlinear functions, or multivariate specifications. For example,<br/>code based on those two procedures is provided for problems ranging from the analysis of count<br/>data in a split-plot design to the joint analysis of survival and repeated measures data; there is<br/>also an implementation for the increasingly popular zero-inflated Poisson models with random<br/>effects! The new chapter on Bayesian analysis of mixed models is also timely and highly<br/>readable for those researchers wishing to explore that increasingly important area of application<br/>for their own research.”<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Robert J. Tempelman<br/>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Michigan State University</p><p>【原创书评】<br/>这本书非常详细的介绍了混合模型（Mixed Models）在sas统计软件中的实现，以及对具体实例的分析结果的解释页都很详细，对重复数据，列</p><p>块分析，随机效应，固定效应都非常详细的介绍了，而且公式，结果，分析的都非常清晰，让人容易明白。在第二版中增加了GLIMMIX 和</p><p>NLMIXED procedures两个过程，以及mixed models计算power的问题，另外case studies的那章更为详细具体的说明mixed models在实际中的应</p><p>用。</p><p></p>

Thanks a lot.<br/>

[em06]