Smoothing Techniques: With Implementation in S

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Springer Science & Business Media, Dec 6, 2012 - Mathematics - 262 pages
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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.
 

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Contents

The Histogram
3
Kernel Density Estimation
43
Further Density Estimators
85
Nonparametric Regression
123
Bandwidth Selection
151
Simultaneous Error Bars
173
Tables
199
Solutions
207
List of Used S Commands
247
References
253
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