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Regression for Categorical Data

By Gerhard Tutz

Regression for Categorical Data Mathematics

Regression for Categorical Data

By Gerhard Tutz

0 - Default Title
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.
Product details
Edition:
illustrated
Number of Pages:
572
Release Date:
2011-11-21
Publication Date:
2015-02-23
Publisher:
Cambridge University Press
Languages:
Original: English
ISBN10:
1107009650
ISBN13:
9781107009653
GPSR Manufacturer Reference:
[email protected]
Weight:
1257 g
Height:
183 cm
Width:
260 cm
Thickness:
35 cm
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