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Full Description
This comprehensive modern look at regression covers a wide range of topics and relevant contemporary applications, going well beyond the topics covered in most introductory books. With concision and clarity, the authors present linear regression, nonparametric regression, classification, logistic and Poisson regression, high-dimensional regression, quantile regression, conformal prediction and causal inference. There are also brief introductions to neural nets, deep learning, random effects, survival analysis, graphical models and time series. Suitable for advanced undergraduate and beginning graduate students, the book will also serve as a useful reference for researchers and practitioners in data science, machine learning, and artificial intelligence who want to understand modern methods for data analysis.
Contents
Preface; Notation; 1. Introduction; 2. Linear regression; 3. Prediction error, cross-validation and model selection; 4. High dimensional linear regression; 5. Logistic and Poisson regression; 6. Univariate nonparametric regression; 7. Nonparametric regression with multiple features; 8. Quantile regression; 9. Classification; 10. Prediction sets and conformal inference; 11. Causal inference; 12. Other topics; Appendix A. Matrix theory; Appendix B. Basic probability and statistics; Data Sources; References; Index.
