1. What is Nonparametric Regression?
1.1 Preliminary Examples
1.1.1 Infant Mortality
1.1.2 Married Women's Labor-Force
Participation
1.1.3 Prestige of Canadian
Occupations
1.2 Plan of This Monograph
1.3 Notes on Background, Approach, and Computing
2. Binning and Local Averaging
2.1 Binning
2.1.1 Statistical Considerations*
2.2 Local Averaging
2.2.1 Moving Averages for
Time-Series Data
3. Kernel Estimation
4. Local Polynomial Regression
4.1 Selecting the Span
4.2 Statistical Issues in Local Regression*
4.3 Bandwidth Revisited*
4.3.1 Selecting the Span
by Cross-Validation
4.4 Making Local Regression Resistant to Outliers
4.4.1 Normal Quantile-Comparison
Plots of Residuals
4.5 Displaying Spread and Asymmetry*
4.6 Smoothing Time-Series Data*
5. Statistical Inference for Local-Polynomial Regression
5.1 Confidence Envelopes
5.2 Hypothesis Tests
5.3 Some Statistical Details and Alternative Inference
Procedures*
5.3.1 The Smoother Matrix
and the Variance of the Fitted Values
5.3.2 Degrees of Freedom
5.3.3 A Caveat
5.3.4 Bootstrap Confidence
Bands
5.3.5 Randomization Tests
6. Splines*
6.1 Regression Splines
6.2 Smoothing Splines
6.3 Equivalent Kernels
7. Nonparametric Regression and Data Analysis
7.1 The "Bulging Rule"
7.2 Partial-Residual Plots
7.3 Concluding Remarks