Error estimates for multivariate regression on discretized function spaces
Error estimates for multivariate regression on discretized function spaces
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dc.contributor.author | Bohn, Bastian | |
dc.contributor.author | Griebel, Michael | |
dc.date.accessioned | 2024-08-23T06:56:28Z | |
dc.date.available | 2024-08-23T06:56:28Z | |
dc.date.issued | 03.2016 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11811/11918 | |
dc.description.abstract | In this paper, we will discuss the discretization error for the regression setting and derive error bounds relying on the approximation properties of the discretized space. Furthermore, we will point out how the sampling error and the discretization error interact and how they can be balanced appropriately. We will present two examples based on tensor product spaces (sparse grids, hyperbolic crosses) which provide a suitable approach in the case of large sample sets in moderate dimensions. | en |
dc.format.extent | 25 | |
dc.language.iso | eng | |
dc.relation.ispartofseries | INS Preprints ; 1412 | |
dc.rights | In Copyright | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | regression | |
dc.subject | learning theory | |
dc.subject | convergence rates | |
dc.subject | discretized function spaces | |
dc.subject | sparse grids | |
dc.subject | bias-variance problem | |
dc.subject.ddc | 510 Mathematik | |
dc.subject.ddc | 518 Numerische Analysis | |
dc.title | Error estimates for multivariate regression on discretized function spaces | |
dc.type | Preprint | |
dc.publisher.name | Institut für Numerische Simulation (INS) | |
dc.publisher.location | Bonn | |
dc.rights.accessRights | openAccess | |
dc.relation.doi | https://doi.org/10.1137/15M1013973 | |
ulbbn.pubtype | Zweitveröffentlichung | |
dcterms.bibliographicCitation.url | https://ins.uni-bonn.de/publication/preprints |
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