Regression for Categorical Data /
Tutz, Gerhard,
Regression for Categorical Data / Gerhard Tutz. - 1 online resource (572 pages) : digital, PDF file(s). - Cambridge Series in Statistical and Probabilistic Mathematics ; 34 . - Cambridge Series in Statistical and Probabilistic Mathematics ; 34. .
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511842061 (ebook)
Regression analysis
Categories (Mathematics)
QA278.2 / .T88 2012
519.5/36
Regression for Categorical Data / Gerhard Tutz. - 1 online resource (572 pages) : digital, PDF file(s). - Cambridge Series in Statistical and Probabilistic Mathematics ; 34 . - Cambridge Series in Statistical and Probabilistic Mathematics ; 34. .
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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.
9780511842061 (ebook)
Regression analysis
Categories (Mathematics)
QA278.2 / .T88 2012
519.5/36