Book contents
- Frontmatter
- Contents
- Preface
- Notation
- 1Introduction
- 2Linear Regression
- 3Prediction Error, Cross-Validation, and Model Selection
- 4High-Dimensional Linear Regression
- 5Logistic and Poisson Regression
- 6Univariate Nonparametric Regression
- 7Nonparametric Regression with Multiple Features
- 8Quantile Regression
- 9Classification
- 10Prediction Sets and Conformal Inference
- 11Causal Inference
- 12Other Topics
- Appendix A:Matrix Theory
- Appendix B:Basic Probability and Statistics
- Data Sources
- Bibliography
- Index
12 - Other Topics
Published online by Cambridge University Press: 08 May 2026
Book contents
- Frontmatter
- Contents
- Preface
- Notation
- 1Introduction
- 2Linear Regression
- 3Prediction Error, Cross-Validation, and Model Selection
- 4High-Dimensional Linear Regression
- 5Logistic and Poisson Regression
- 6Univariate Nonparametric Regression
- 7Nonparametric Regression with Multiple Features
- 8Quantile Regression
- 9Classification
- 10Prediction Sets and Conformal Inference
- 11Causal Inference
- 12Other Topics
- Appendix A:Matrix Theory
- Appendix B:Basic Probability and Statistics
- Data Sources
- Bibliography
- Index
Summary
In this chapter, we briefly cover a few other topics related to regression. Each topic is the subject of entire textbooks. Our goal is to give a very concise introduction to each topic. The topics include random effects and empirical Bayes, neural nets and deep learning, survival analysis, graphical models, and time series.
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- Chapter
- Information
- All of Regression , pp. 173 - 188Publisher: Cambridge University PressPrint publication year: 2026
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