Tekijä: Ludwig Fahrmeir; Rita Kunstler; Iris Pigeot; Gerhard Tutz; Angelika Caputo; Stefan Lang; Rita Ka / nstler Kustantaja: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2005) Saatavuus: Ei tiedossa
Tekijä: Angelika Caputo; Ludwig Fahrmeir; Rita Künstler; Stefan Lang; Iris Pigeot-Kübler; Gerhard Tutz Kustantaja: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2008) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Ludwig Fahrmeir; Brian Francis; Robert Gilchrist; Gerhard Tutz Kustantaja: Springer-Verlag New York Inc. (1992) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Ludwig Fahrmeir; Rita Künstler; Iris Pigeot; Gerhard Tutz; Angelika Caputo; Stefan Lang Kustantaja: Springer-Verlag GmbH (2013) Saatavuus: Ei tiedossa
Tekijä: Ludwig Fahrmeir; Christian Heumann; Rita Künstler; Iris Pigeot; Gerhard Tutz Kustantaja: Springer Fachmedien Wiesbaden (2016) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Ludwig Fahrmeir; Christian Heumann; Rita Künstler; Iris Pigeot; Gerhard Tutz Kustantaja: Springer Spektrum (2024) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Ludwig Fahrmeir; Rita Kunstler; Iris Pigeot; Professor Gerhard Tutz Kustantaja: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG (2009) Saatavuus: | Arvioimme, että tuote lähetetään meiltä noin 1-3 viikossa
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.