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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.
| Autorius: | Wolfgang Härdle |
| Serija: | Springer Series in Statistics |
| Leidėjas: | Springer US |
| Išleidimo metai: | 2011 |
| Knygos puslapių skaičius: | 276 |
| ISBN-10: | 1461287685 |
| ISBN-13: | 9781461287681 |
| Formatas: | 235 x 155 x 16 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Smoothing Techniques: With Implementation in S“
