Smoothing Techniques: With Implementation in S (Springer Series in Statistics),New

Smoothing Techniques: With Implementation in S (Springer Series in Statistics),New

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The author has attempted to present a book that provides a nontechnical introduction into the area of nonparametric 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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