1. Introduction
1.1 Two Examples
1.1.1 Occupational Prestige
1.1.2 Married Women's Labor-Force
Participation
1.2 Plan of the Monograph
1.2.1 What is Included?
1.2.2 What is Missing?
1.3 Notes on Background, Approach, and Computing
2. Local Polynomial Multiple Regression
2.1 Review of Local Polynomial Simple Regression
2.1.1 Selecting Order and
Span
2.1.2 Making Local Polynomial
Estimates Resistant to Outliers
2.1.3 Statistical Inference
2.2 Kernel Weights in Multiple Regression
2.3 Span Selection, Statitical Inference, and Order
Selection
2.3.1 Span
2.3.2 Inference
2.3.3 Order
2.4 Obstacles to Nonparametric Multiple Regression
2.5 An Illustration: Occupational Prestige
3. Additive Regression Models
3.1 Fitting the Additive Regression Model
3.2 Some Statistical Details*
3.2.1 Backfitting
3.2.2 Statistical Inference
3.3 Semiparametric Models and Models With Interactions
4. Projection-Pursuit Regression
4.1 Fitting the Projection-Pursuit Regression Model*
4.2 Illustrations of Projection-Pursuit Regression
4.2.1 A Simple Muiltiplicative
Model
4.2.2 Occupational Prestige
Reprised
5. Regression Trees
5.1 Growing and Pruning Trees
5.2 Reservations about Regression Trees
6. Generalized Nonparametric Regression*
6.1 Local Likelihood Estimation
6.2 Generalized Additive Models
6.2.1 Statistical Inference
6.2.2 An Illustration: Labor-Force
Participation
6.3 Classification Trees
7. Concluding Remarks: Integrating Nonparametric Regression in Statistical
Practice