Skip to main content Accessibility help
×
  1. Home
  2. Books
  3. Regression for Categorical Data
  • Cited by 87
      • Show more authors
      • You may already have access via personal or institutional login
    • Select format
    • Publisher:
      Cambridge University Press
      Publication date:
      June 2012
      November 2011
      ISBN:
      9780511842061
      9781107009653
      DOI:
      https://doi.org/10.1017/CBO9780511842061
      Dimensions:
      (253 x 215 mm)
      Weight & Pages:
      1.16kg, 572 Pages
      Dimensions:
      Weight & Pages:
      • Series:
      Cambridge Series in Statistical and Probabilistic Mathematics (34)
      Subjects:
      General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
    You may already have access via personal or institutional login
    • Selected: Digital
    Add to cart View cart Buy from Cambridge.org
    Series:
    Cambridge Series in Statistical and Probabilistic Mathematics (34)
    Subjects:
    General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
    Information

    Book description

    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.

    Reviews

    "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

    Refine List

    Actions for selected content:

    Select all | Deselect all
    • View selected items
    • Export citations
    • Download PDF (zip)
    • Save to Kindle
    • Save to Dropbox
    • Save to Google Drive

    Save Search

    You can save your searches here and later view and run them again in "My saved searches".

    Please provide a title, maximum of 40 characters.
    Cancel
    ×

    Contents

    Contents

    Metrics

    Altmetric attention score

    Full text views

    Total number of HTML views: 0
    Total number of PDF views: 0 *
    Loading metrics...

    Book summary page views

    Total views: 0 *
    Loading metrics...

    * Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

    Usage data cannot currently be displayed.

    Accessibility standard: Unknown

    Why this information is here

    This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

    Accessibility Information

    Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.