Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bissantz, Nicolai | - |
dc.contributor.author | Chown, Justin | - |
dc.contributor.author | Dette, Holger | - |
dc.date.accessioned | 2016-10-28T09:00:10Z | - |
dc.date.available | 2016-10-28T09:00:10Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://hdl.handle.net/2003/35302 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-17345 | - |
dc.description.abstract | Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a regularization parameter used in our indirect regression function estimator. The resulting methodology is based on a smooth bootstrap of the model residuals. A simulation study demonstrates the effectiveness of our approach. | de |
dc.language.iso | en | de |
dc.relation.ispartofseries | Discussion Paper / SFB823;56, 2016 | - |
dc.subject | bandwidth selection | de |
dc.subject | smooth bootstrap | de |
dc.subject | residual-based empirical distribution function | de |
dc.subject | regularization | de |
dc.subject | indirect nonparametric regression | de |
dc.subject | deconvolution function estimator | de |
dc.subject.ddc | 310 | - |
dc.subject.ddc | 330 | - |
dc.subject.ddc | 620 | - |
dc.title | Regularization parameter selection in indirect regression by residual based bootstrap | de |
dc.type | Text | de |
dc.type.publicationtype | workingPaper | de |
dc.subject.rswk | Nichtparametrische Regression | de |
dc.subject.rswk | Bootstrap-Statistik | de |
dcterms.accessRights | open access | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_5616_SFB823_Bissantz_Chown_Dette.pdf | DNB | 433.23 kB | Adobe PDF | View/Open |
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