Smoothing Techniques With Implementation in S
Titre:
Smoothing Techniques With Implementation in S
ISBN (Numéro international normalisé des livres):
9781461244325
Auteur personnel:
Edition:
1st ed. 1991.
PRODUCTION_INFO:
New York, NY : Springer New York : Imprint: Springer, 1991.
Description physique:
XII, 262 p. online resource.
Collections:
Springer Series in Statistics,
Table des matières:
I. Density Smoothing -- 1. The Histogram -- 2. Kernel Density Estimation -- 3. Further Density Estimators -- 4. Bandwidth Selection in Practice -- II. Regression Smoothing -- 5. Nonparametric Regression -- 6. Bandwidth Selection -- 7. Simultaneous Error Bars -- Tables -- Solutions -- List of Used S Commands -- Symbols and Notation -- References.
Extrait:
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.
Auteur collectif ajouté:
Langue:
Anglais