Regression for categorical data. (English) Zbl 1304.62021

Cambridge Series in Statistical and Probabilistic Mathematics 34. Cambridge: Cambridge University Press (ISBN 978-1-107-00965-3/hbk; 978-1-139-12007-4/ebook). x, 561 p. (2011).
Publisher’s 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, 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.


62-02 Research exposition (monographs, survey articles) pertaining to statistics
62G08 Nonparametric regression and quantile regression
62H12 Estimation in multivariate analysis
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62J02 General nonlinear regression
62J12 Generalized linear models (logistic models)


catdata; R
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