Smoothing Techniques

1. Front Matter

   Pages i-xi

   PDF

2. Density Smoothing

   1. Front Matter

      Pages 1-1

      PDF

   2. The Histogram

      • Wolfgang Härdle

      Pages 3-42

   3. Kernel Density Estimation

      • Wolfgang Härdle

      Pages 43-84

   4. Further Density Estimators

      • Wolfgang Härdle

      Pages 85-89

   5. Bandwidth Selection in Practice

      • Wolfgang Härdle

      Pages 90-119

3. Regression Smoothing

   1. Front Matter

      Pages 121-121

      PDF

   2. Nonparametric Regression

      • Wolfgang Härdle

      Pages 123-150

   3. Bandwidth Selection

      • Wolfgang Härdle

      Pages 151-172

   4. Simultaneous Error Bars

      • Wolfgang Härdle

      Pages 173-195

4. Back Matter

   Pages 197-261

   PDF

The author has attempted to present a book that provides a non-technical introduction into the area of non-parametric density and regression function estimation. The application of these methods is discussed in terms of the S computing environment. Smoothing in high dimensions faces the problem of data sparseness. A principal feature of smoothing, the averaging of data points in a prescribed neighborhood, is not really practicable in dimensions greater than three if we have just one hundred data points. Additive models provide a way out of this dilemma; but, for their interactiveness and recursiveness, they require highly effective algorithms. For this purpose, the method of WARPing (Weighted Averaging using Rounded Points) is described in great detail.

• Area
• Bootstrapping
• Counting
• Lemma
• algorithms
• approximation
• average
• estimator
• function
• implementation
• kernel
• likelihood
• selection
• statistics
• techniques

• Center for Operations Research and Econometrics, Université Catholique de Louvain, Louvain-La-Neuve, Belgium

  Wolfgang Härdle

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