Bias in nearest-neighbor hazard estimation
| dc.contributor.author | Dette, Holger | |
| dc.contributor.author | Weißbach, Rafael | |
| dc.date.accessioned | 2008-11-26T14:48:51Z | |
| dc.date.available | 2008-11-26T14:48:51Z | |
| dc.date.issued | 2008-11-26T14:48:51Z | |
| dc.description.abstract | In nonparametric curve estimation, the smoothing parameter is critical for performance. In order to estimate the hazard rate, we compare nearest neighbor selectors that minimize the quadratic, the Kullback-Leibler, and the uniform loss. These measures result in a rule of thumb, a crossvalidation, and a plug-in selector. A Monte Carlo simulation within the threeparameter exponentiated Weibull distribution indicates that a counterfactual normal distribution, as an input to the selector, does provide a good rule of thumb. If bias is the main concern, minimizing the uniform loss yields the best results, but at the cost of very high variability. Crossvalidation has a similar bias to the rule of thumb, but also with high variability. AMS: 62M02 | en |
| dc.identifier.uri | http://hdl.handle.net/2003/25878 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-14464 | |
| dc.language.iso | en | de |
| dc.subject | Bandwidth selection | en |
| dc.subject | Hazard rate | en |
| dc.subject | Kernel estimation | en |
| dc.subject | Nearest neighbor bandwidth | en |
| dc.subject | Rule of thumb | en |
| dc.subject | Variable bandwidth | en |
| dc.subject.ddc | 004 | |
| dc.title | Bias in nearest-neighbor hazard estimation | en |
| dc.type | Text | de |
| dc.type.publicationtype | report | en |
| dcterms.accessRights | open access |