Regression for categorical data
 Responsibility
 Gerhard Tutz.
 Language
 English.
 Imprint
 Cambridge ; New York : Cambridge University Press, 2012.
 Physical description
 x, 561 p. : ill ; 26 cm.
 Series
 Cambridge series on statistical and probabilistic mathematics.
Access
Available online
 dx.doi.org Cambridge Books Online Access limited to one user.
Math & Statistics Library

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QA278.2 .T88 2012

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QA278.2 .T88 2012
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Creators/Contributors
 Author/Creator
 Tutz, Gerhard.
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 513544) and indexes.
 Contents

 1. Introduction 2. Binary regression: the logit model 3. Generalized linear models 4. Modeling of binary data 5. Alternative binary regression models 6. Regularization and variable selection for parametric models 7. Regression analysis of count data 8. Multinomial response models 9. Ordinal response models 10. Semi and nonparametric generalized regression 11. Treebased methods 12. The analysis of contingency tables: loglinear and graphical models 13. Multivariate response models 14. Random effects models 15. Prediction and classification Appendix A. Distributions Appendix B. Some basic tools Appendix C. Constrained estimation Appendix D. KullbackLeibler distance and informationbased criteria of model fit Appendix E. Numerical integration and tools for random effects modeling.
 (source: Nielsen Book Data)
 Publisher's Summary
 This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and highdimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zeroinflated regression models for count data; and nonstandard treebased ensemble methods, which provide excellent tools for prediction and the handling of both nominal and ordered categorical predictors. The book is accompanied by an R package that contains data sets and code for all the examples.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2012
 Title Variation
 Categorical data
 Series
 Cambridge series in statistical and probabilistic mathematics
 ISBN
 9781107009653 (hardback)
 1107009650 (hardback)