Preface
1 Statistical Learning as a Regression Problem
1.1 Getting Started
1.2 Setting the Regression Context
1.3 The Transition to Statistical Learning
1.3.1 Some Goals of Statistical Learning
1.3.2 Statistical Inference
1.3.3 Some Initial Cautions
1.3.4 A Cartoon Illustration
1.3.5 A Taste of Things to Come
1.4 Some Initial Concepts and Definitions
1.4.1 Overall Goals
1.4.2 Loss Functions and Related Concepts
1.4.3 Linear Estimators
1.4.4 Degrees of Freedom
1.4.5 Model Evaluation
1.4.6 Model Selection
1.4.7 Basis Functions
1.5 Some Common Themes
1.6 Summary and Conclusions
2 Regression Splines and Regression Smoothers
2.1 Introduction
2.2 Regression Splines
2.2.1 Applying a Piecewise Linear Basis
2.2.2 Polynomial Regression Splines
2.2.3 Natural Cubic Splines
2.2.4 B-Splines
2.3 Penalized Smoothing
2.3.1 Shrinkage
2.3.2 Shrinkage and Statistical Inference
2.3.3 Shrinkage: So What?
2.4 Smoothing Splines
2.4.1 An Illustration
2.5 Locally Weighted Regression as a Smoother
2.5.1 Nearest Neighbor Methods
2.5.2 Locally Weighted Regression
2.6 Smoothers for Multiple Predictors
2.6.1 Smoothing in Two Dimensions
2.6.2 The Generalized Additive Model
2.7 Smoothers with Categorical Variables
2.7.1 An Illustration
2.8 Locally Adaptive Smoothers
2.9 The Role of Statistical Inference
2.9.1 Some Apparent Prerequisites
2.9.2 Confidence Intervals
2.9.3 Statistical Tests
2.9.4 Can Asymptotics Help?
2.10 Software Issues
2.11 Summary and Conclusions
3 Classification and Regression Trees (CART)
