Nonparametric regression as an example of model choice
| dc.contributor.author | Davies, P. L. | |
| dc.contributor.author | Gather, U. | |
| dc.contributor.author | Weinert, H. | |
| dc.date.accessioned | 2006-05-04T09:55:49Z | |
| dc.date.available | 2006-05-04T09:55:49Z | |
| dc.date.issued | 2006-05-04T09:55:49Z | |
| dc.description.abstract | Nonparametric regression can be considered as a problem of model choice. In this paper we present the results of a simulation study in which several nonparametric regression techniques including wavelets and kernel methods are compared with respect to their behaviour on different test beds. We also include the taut-string method whose aim is not to minimize the distance of an estimator to some “true” generating function f but to provide a simple adequate approximation to the data. Test beds are situations where a “true” generating f exists and in this situation it is possible to compare the estimates of f with f itself. The measures of performance we use are the L^2 and the L^infinity norms and the ability to identify peaks. | en |
| dc.format.extent | 542506 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2003/22400 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-14228 | |
| dc.language.iso | en | |
| dc.subject | Kernel method | en |
| dc.subject | Nonparametric regression | en |
| dc.subject | Simulation study | en |
| dc.subject | Taut string method | en |
| dc.subject | Wavelet | en |
| dc.subject.ddc | 004 | |
| dc.title | Nonparametric regression as an example of model choice | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access | |
| eldorado.dnb.deposit | true |