| 000 -LEADER |
|---|
| fixed length control field |
02555 a2200217 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
151103b2012 xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1107009650 |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
|---|
| International Standard Serial Number |
9780511842061 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.536 |
| Item number |
T8R3 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Tutz, Gerhard |
| 9 (RLIN) |
324058 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Regression for categorical data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Cambridge |
| Name of publisher, distributor, etc |
Cambridge University Press |
| Date of publication, distribution, etc |
2012 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
x, 561 p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
Cambridge Series in Statistical and Probabilistic Mathematics; 34 |
| 9 (RLIN) |
323606 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Table of contents: <br/><br/>1. Introduction <br/>2. Binary Regression: The Logit Model <br/>3. Generalized Linear Models <br/>4. Modeling of Binary Data <br/>5. Alternative Binary Regression Models <br/>6. Regularization and Variable Selection for Parametric Models <br/>7. Regression Analysis of Count Data <br/>8. Multinomial Response Models <br/>9. Ordinal Response Models <br/>10. Semi- and Non-Parametric Generalized Regression <br/>11. Tree-Based Methods <br/>12. The Analysis of Contingency Tables: Log-Linear and Graphical Models <br/>13. Multivariate Response Models <br/>14. Random Effects Models and Finite Mixtures <br/>15. Prediction and Classification <br/><br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Statistical theory and methods |
| 9 (RLIN) |
323603 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Statistical and probabilistic mathematics |
| 9 (RLIN) |
323649 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
E-Book |
| Uniform Resource Identifier |
<a href="http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511842061">http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511842061</a> |
| Access method |
Unlimited (Internet) |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
eBooks |
