<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/> 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/> * random effect only and random coefficients models</p><p> * split-plot, multilocation, and repeated measures models</p><p> * hierarchical models with nested random effects</p><p> * analysis of covariance models</p><p> * spatial correlation models</p><p> * 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/> Geert Verbeke<br/> Biostatistical Centre, Katholieke Universiteit Leuven, Belgium<br/> Geert Molenberghs<br/> 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/> Robert J. Tempelman<br/> 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>
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