Smoothing Techniques : With Implementation in S /
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 d...
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| Format: | eBook |
| Language: | English |
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New York, NY :
Springer New York,
1991.
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| Series: | Springer series in statistics.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (xi, 261 pages 87 illustrations) |
| ISBN: | 9781461244325 (electronic bk.) 1461244323 (electronic bk.) |
| ISSN: | 0172-7397 |