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Regression for Categorical Data

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Author: Gerhard Tutz, Ludwig-Maximilians-Universität Munchen
  • Date Published: February 2012
  • availability: Available
  • format: Hardback
  • isbn: 9781107009653

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  • 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. The book is accompanied by an R package that contains data sets and code for all the examples.

    • Covers modern topics such as high-dimensional regression and nonparametric models
    • Can be used as a text for courses on categorical data for students from different fields
    • Written from the perspective of an applied statistician for a focus on basic concepts and applications, rather than formal mathematical theory
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    Product details

    • Date Published: February 2012
    • format: Hardback
    • isbn: 9781107009653
    • length: 572 pages
    • dimensions: 257 x 185 x 36 mm
    • weight: 1.16kg
    • contains: 98 b/w illus. 102 tables 77 exercises
    • availability: Available
  • 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.

  • Instructors have used or reviewed this title for the following courses

    • Advanced Regression and Design
    • Advanced Social Statistics
    • Applied Biostatistics
    • Categorical Data Analysis
    • Generalized Linear Models
    • Quantitative Methods
    • Statistical Learning I
  • Author

    Gerhard Tutz, Ludwig-Maximilians-Universität Munchen
    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.

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