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.

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.

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



Book Information
ISBN 9781107009653
Author Gerhard Tutz
Format Hardback
Page Count 572
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 1160g
Dimensions(mm) 257mm * 185mm * 36mm