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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Dr Gerhard Tutz is a Professor of Mathematics in the Department of Statistics at Ludwig-Maximilians University, Munich. He is formerly a Professor at the Technical University Berlin. He is the author or co-author of nine books and more than 100 papers.
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Über den AutorDr Gerhard Tutz is a Professor of Mathematics in the Department of Statistics at Ludwig-Maximilians University, Munich. He is formerly a Professor at the Technical University Berlin. He is the author or co-author of ni. N° de réf. du vendeur 447213260
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Hardcover. Etat : new. Hardcover. 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. 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 item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781107009653
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