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雷达卡
搜了一下，坛子里好像没有这本书。
Log-Linear Modeling: Concepts, Interpretation, and Application [Hardcover] by Alexander von Eye , Eun-Young Mun
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Review “This book provides an essential, easily accessible introductory treatment of log-linear modelling. . . The book is written at a level that should pose no major problems to students after introductory statistics courses.” (International Statistical Review, 25 June 2013)
“It is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It also serves as an excellent reference for applied researchers in virtually any area of study, from medicine and statistics to the social sciences, who analyze empirical data in their everyday work.” (Zentralblatt Math, 1 May 2013)
From the Back Cover An easily accessible introduction to log-linear modeling for non-statisticians
Highlighting advances that have lent to the topic's distinct, coherent methodology over the past decade, Log-Linear Modeling: Concepts, Interpretation, and Application provides an essential, introductory treatment of the subject, featuring many new and advanced log-linear methods, models, and applications.
The book begins with basic coverage of categorical data, and goes on to describe the basics of hierarchical log-linear models as well as decomposing effects in cross-classifications and goodness-of-fit tests. Additional topics include:
- The generalized linear model (GLM) along with popular methods of coding such as effect coding and dummy coding
- Parameter interpretation and how to ensure that the parameters reflect the hypotheses being studied
- Symmetry, rater agreement, homogeneity of association, logistic regression, and reduced designs models
Log-Linear Modeling is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It also serves as an excellent reference for applied researchers in virtually any area of study, from medicine and statistics to the social sciences, who analyze empirical data in their everyday work.
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