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, which provide excellent tools for
prediction and the handling of both nominal and ordered categorical
predictors. The book is accompanied an R package that contains data
sets and code for all the examples.
General
Imprint: |
Cambridge UniversityPress
|
Country of origin: |
United Kingdom |
Series: |
Cambridge Series in Statistical and Probabilistic Mathematics |
Release date: |
November 2011 |
First published: |
November 2011 |
Authors: |
Gerhard Tutz
(Professor)
|
Dimensions: |
257 x 185 x 36mm (L x W x T) |
Format: |
Hardcover
|
Pages: |
572 |
Edition: |
New |
ISBN-13: |
978-1-107-00965-3 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
1-107-00965-0 |
Barcode: |
9781107009653 |
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