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Regression for Categorical Data

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Title: Regression for Categorical Data
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.
Authors: Gerhard Tutz
Resource Type: eBook.
Subjects: Categories (Mathematics), Regression analysis
Categories: MATHEMATICS / Probability & Statistics / Regression Analysis
Database: eBook Collection (EBSCOhost)
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DbLabel: eBook Collection (EBSCOhost)
An: 408863
RelevancyScore: 1044
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1044.26904296875
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  Data: 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.
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      – Code: 519.536
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Categories (Mathematics)
        Type: general
      – SubjectFull: Regression analysis
        Type: general
    Titles:
      – TitleFull: Regression for Categorical Data
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Gerhard Tutz
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            NameFull: Gerhard Tutz
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2012
            – D: 04
              M: 02
              Type: profile
              Y: 2014
          Identifiers:
            – Type: isbn-print
              Value: 9781107009653
            – Type: isbn-electronic
              Value: 9781139128551
          Titles:
            – TitleFull: Regression for Categorical Data
              Type: main
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