Abstract
A novel concept for estimating smooth functions by selection techniques based on boosting is developed. It is suggested to put radial basis functions with different spreads at each knot and to do selection and estimation simultaneously by a componentwise boosting algorithm. The methodology of various other smoothing and knot selection procedures (e.g. stepwise selection) is summarized. They are compared to the proposed approach by extensive simulations for various unidimensional settings, including varying spatial variation and heteroskedasticity, as well as on a real world data example. Finally, an extension of the proposed method to surface fitting is evaluated numerically on both, simulation and real data. The proposed knot selection technique is shown to be a strong competitor to existing methods for knot selection.
| Item Type: | Paper |
|---|---|
| Faculties: | Mathematics, Computer Science and Statistics > Statistics > Collaborative Research Center 386 Special Research Fields > Special Research Field 386 |
| Subjects: | 500 Science > 510 Mathematics |
| URN: | urn:nbn:de:bvb:19-epub-1849-4 |
| Language: | English |
| Item ID: | 1849 |
| Date Deposited: | 11. Apr 2007 |
| Last Modified: | 04. Nov 2020 12:46 |

