Skip to content
Register Sign in Wishlist

Regression for Categorical Data

$89.99 (P)

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Author: Gerhard Tutz, Ludwig-Maximilians-Universität Munchen
  • Date Published: November 2011
  • availability: Available
  • format: Hardback
  • isbn: 9781107009653

$ 89.99 (P)
Hardback

Add to cart Add to wishlist

Other available formats:
eBook


Looking for an examination copy?

If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • 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.

    • 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
    Read more

    Reviews & endorsements

    "Regression for Categorical Data is a well-written and nicely organized book. It focuses on the regression analysis of categorical data, including both binary and count data, and introduced up-to-date developments in the field."
    Xia Wang, Mathematical Reviews

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: November 2011
    • 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.

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×