Book contents
- Frontmatter
- Contents
- Preface
- 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 Non-Parametric 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 and Finite Mixtures
- 15 Prediction and Classification
- A Distributions
- B Some Basic Tools
- C Constrained Estimation
- D Kullback-Leibler Distance and Information-Based Criteria of Model Fit
- E Numerical Integration and Tools for Random Effects Modeling
- List of Examples
- Bibliography
- Author Index
- Subject Index
Contents
Published online by Cambridge University Press: 05 June 2012
Book contents
- Frontmatter
- Contents
- Preface
- 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 Non-Parametric 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 and Finite Mixtures
- 15 Prediction and Classification
- A Distributions
- B Some Basic Tools
- C Constrained Estimation
- D Kullback-Leibler Distance and Information-Based Criteria of Model Fit
- E Numerical Integration and Tools for Random Effects Modeling
- List of Examples
- Bibliography
- Author Index
- Subject Index
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- Regression for Categorical Data , pp. v - viiiPublisher: Cambridge University PressPrint publication year: 2011