Smoothing Techniques: With Implementation in S - Wolfgang Härdle
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Toimitus 12-18 arkipäivässä
30 päivän palautusoikeus
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, i ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Wolfgang Härdle |
|---|---|
| Julkaisija | Springer US |
| Series | Springer Series in Statistics |
| Julkaisuvuosi | 2011 |
| Kannen tyyppi | Pehmeäkantinen |
| EAN | 9781461287681 |