Smoothing Techniques
With Implementation in S
(Sprache: Englisch)
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....
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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.
Inhaltsverzeichnis zu „Smoothing Techniques “
- Preface- Density Smoothing: The Histogram. Kernel Density Estimation. Further Density Estimators. Bandwidth Selection in Practice
- Regression Smoothing: Nonparametric Regression. Bandwidth Selection. Simultaneous Error Bars
- Appendix
Bibliographische Angaben
- Autor: Wolfgang Härdle
- 1991, 276 Seiten, Maße: 16,1 x 24 cm, Gebunden, Englisch
- Verlag: Springer, New York
- ISBN-10: 0387973672
- ISBN-13: 9780387973678
- Erscheinungsdatum: 05.12.1990
Sprache:
Englisch
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