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Regression for Categorical Data (Cambridge Series in Statistical and Probabilistic Mathematic)

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Synopses & Reviews

Publisher Comments:

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 high-dimensional 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 zero-inflated regression models for count data; and non-standard tree-based ensemble methods, which provide excellent tools for prediction and the handling of both nominal and ordered categorical predictors. The book is accompanied an R package that contains data sets and code for all the examples.

Synopsis:

The book treats many recent developments in flexible and high-dimensional regression not normally included in books on categorical data analysis.

About the Author

Dr Gerhard Tutz is a Professor of Mathematics in the Department of Statistics at Ludwig-Maximilians University, Munich. He is formerly a Professor at the Technical University Berlin. He is the author or co-author of nine books and more than 100 papers.

Table of 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. Tree-based methods; 12. The analysis of contingency tables: log-linear 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. Kullback-Leibler distance and information-based criteria of model fit; Appendix E. Numerical integration and tools for random effects modeling.

Product Details

ISBN:
9781107009653
Author:
Tutz, Gerhard
Publisher:
Cambridge University Press
Location:
New York
Subject:
Mathematics | Probability and Statistics
Subject:
Statistics
Series:
Cambridge Series in Statistical and Probabilistic Mathematics
Publication Date:
20111131
Binding:
HARDCOVER
Language:
English
Illustrations:
98 b/w illus. 102 tables 77 exercises
Pages:
512
Dimensions:
9.96 x 8.46 in

Related Subjects


Metaphysics » General
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Science and Mathematics » Mathematics » Probability and Statistics » General
Science and Mathematics » Mathematics » Probability and Statistics » Statistics

Regression for Categorical Data (Cambridge Series in Statistical and Probabilistic Mathematic) New Hardcover
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Product details 512 pages Cambridge University Press - English 9781107009653 Reviews:
"Synopsis" by , The book treats many recent developments in flexible and high-dimensional regression not normally included in books on categorical data analysis.
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