Regression for Categorical Data
£74.99
Part of Cambridge Series in Statistical and Probabilistic Mathematics
- Author: Gerhard Tutz, Ludwig-Maximilians-Universität Munchen
- Date Published: February 2012
- availability: Available
- format: Hardback
- isbn: 9781107009653
£
74.99
Hardback
Other available formats:
eBook
Looking for an inspection copy?
This title is not currently available on inspection
-
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.
Read more- Covers modern topics such as high-dimensional regression and nonparametric models
- Can be used as a text for courses on categorical data for students from different fields
- Written from the perspective of an applied statistician for a focus on basic concepts and applications, rather than formal mathematical theory
Customer reviews
Not yet reviewed
Be the first to review
Review was not posted due to profanity
×Product details
- Date Published: February 2012
- format: Hardback
- isbn: 9781107009653
- length: 572 pages
- dimensions: 257 x 185 x 36 mm
- weight: 1.16kg
- contains: 98 b/w illus. 102 tables 77 exercises
- availability: Available
Table of Contents
1. Introduction
2. Binary regression: the logit model
3. Generalized linear models
4. Modeling of binary data
5. Alternative binary regression models
6. Regularization and variable selection for parametric models
7. Regression analysis of count data
8. Multinomial response models
9. Ordinal response models
10. Semi- and nonparametric generalized regression
11. Tree-based methods
12. The analysis of contingency tables: log-linear and graphical models
13. Multivariate response models
14. Random effects models
15. Prediction and classification
Appendix A. Distributions
Appendix B. Some basic tools
Appendix C. Constrained estimation
Appendix D. Kullback–Leibler distance and information-based criteria of model fit
Appendix E. Numerical integration and tools for random effects modeling.Instructors have used or reviewed this title for the following courses
- Advanced Regression and Design
- Advanced Social Statistics
- Applied Biostatistics
- Categorical Data Analysis
- Generalized Linear Models
- Quantitative Methods
- Statistical Learning I
Sorry, this resource is locked
Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org
Register Sign in» Proceed
You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.
Continue ×Are you sure you want to delete your account?
This cannot be undone.
Thank you for your feedback which will help us improve our service.
If you requested a response, we will make sure to get back to you shortly.
×