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| Preface | |
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| Acknowledgments | |
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| Exordium | |
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| Introduction | |
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| Are the moving average and Fourier series sufficiently useful? | |
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| Is a histogram or normal distribution sufficiently powerful? | |
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| Is interpolation sufficiently powerful? | |
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| Should we use a descriptive equation? | |
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| Parametric regression and nonparametric regression | |
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| Smoothing for data with an equispaced predictor | |
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| Introduction | |
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| Moving average and binomial filter | |
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| Hat matrix | |
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| Local linear regression | |
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| Smoothing spline | |
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| Analysis on eigenvalue of hat matrix | |
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| Examples of S-Plus object | |
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| References | |
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| Problems | |
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| Nonparametric regression for one-dimensional predictor | |
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| Introduction | |
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| Trade-off between bias and variance | |
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| Index to select beneficial regression equations | |
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| Nadaraya-Watson estimator | |
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| Local polynomial regression | |
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| Natural spline and smoothing spline | |
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| LOESS | |
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| Supersmoother | |
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| LOWESS | |
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| Examples of S-Plus object | |
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| References | |
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| Problems | |
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| Multidimensional smoothing | |
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| Introduction | |
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| Local polynomial regression for multidimensional predictor | |
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| Thin plate smoothing splines | |
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| LOESS and LOWESS with plural predictors | |
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| Kriging | |
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| Additive model | |
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| ACE | |
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| Projection pursuit regression | |
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| Examples of S-Plus object | |
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| References | |
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| Problems | |
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| Nonparametric regression with predictors represented as distributions | |
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| Introduction | |
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| Use of distributions as predictors | |
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| Nonparametric DVR method | |
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| Form of nonparametric regression with predictors represented as distributions | |
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| Examples of S-Plus object | |
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| References | |
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| Problems | |
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| Smoothing of histograms and nonparametric probability density functions | |
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| Introduction | |
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| Histogram | |
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| Smoothing a histogram | |
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| Nonparametric probability density function | |
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| Examples of S-Plus object | |
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| References | |
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| Problems | |
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| Pattern recognition | |
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| Introduction | |
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| Bayes' decision rule | |
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| Linear discriminant rule and quadratic discriminant rule | |
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| Classification using nonparametric probability density function | |
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| Logistic regression | |
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| Neural networks | |
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| Tree-based model | |
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| k-nearest-neighbor classifier | |
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| Nonparametric regression based on the least squares | |
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| Transformation of feature vectors | |
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| Examples of S-Plus object | |
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| References | |
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| Problems | |
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| Creation and applications of B-spline bases | |
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| Introduction | |
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| Method to create B-spline basis | |
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| Natural spline created by B-spline | |
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| Application to smoothing spline | |
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| Examples of S-Plus object | |
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| References | |
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| R objects | |
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| Introduction | |
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| Transformation of S-Plus objects in Chapter 2 | |
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| Transformation of S-Plus objects in Chapter 3 | |
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| Transformation of S-Plus objects in Chapter 4 | |
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| Transformation of S-Plus objects in Chapter 5 | |
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| Transformation of S-Plus objects in Chapter 6 | |
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| Transformation of S-Plus objects in Chapter 7 | |
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| Transformation of S-Plus objects in Appendix A | |
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| Further readings | |
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| Index | |