Regression for Categorical Data [Hardcover]

The book treats many recent developments in flexible and high-dimensional regression not normally included in books on categorical data analysis.This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression and recent developments in flexible and high-dimensional regression. Among the topics treated are nonparametric regression; the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods.This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression and recent developments in flexible and high-dimensional regression. Among the topics treated are nonparametric regression; the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods.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 bookl³ê