You might also like

More about Regression for Categorical Data

• Book details
• Synopsis
• Trending among others

You get 303 loyalty points

Book synopsis

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.

Book details

Book category Books in English Mathematics & science Mathematics Probability & statistics

• Full title: Regression for Categorical Data
• Author: Gerhard Tutz
• Language: English
• Binding: Hardback
• Number of pages: 572
• EAN: 9781107009653
• ISBN: 1107009650
• ID: 04385540
• Publisher: Cambridge University Press
• Weight: 1164 g
• Dimensions: 256 × 186 × 23 mm
• Date of publishing: 21. November 2011

Trending among others

---

---

---