Buch, Englisch, 262 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1270 g
With Implementation in S
Buch, Englisch, 262 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 1270 g
Reihe: Springer Series in Statistics
ISBN: 978-0-387-97367-8
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
