Sparse Semi-Functional Partial Linear Single-Index Regression
| UDC.coleccion | Investigación | es_ES |
| UDC.conferenceTitle | Proceedings XoveTIC Conference 2018 | es_ES |
| UDC.departamento | Matemáticas | es_ES |
| UDC.grupoInv | Modelización, Optimización e Inferencia Estatística (MODES) | es_ES |
| UDC.issue | 2 | es_ES |
| UDC.journalTitle | Proceedings | es_ES |
| UDC.startPage | 1190 | es_ES |
| UDC.volume | 18 | es_ES |
| dc.contributor.author | Novo Díaz, Silvia | |
| dc.contributor.author | Aneiros, Germán | |
| dc.contributor.author | Vieu, Philippe | |
| dc.date.accessioned | 2018-10-09T15:54:25Z | |
| dc.date.available | 2018-10-09T15:54:25Z | |
| dc.date.issued | 2018-09-17 | |
| dc.description | Trátase dun resumo estendido da ponencia | |
| dc.description.abstract | [Abstract] The variable selection problem is studied in the sparse semi-functional partial linear model, with single-index type influence of the functional covariate in the response. The penalized least squares procedure is employed for this task. Some properties of the resultant estimators are derived: the existence (and rate of convergence) of a consistent estimator for the parameters in the linear part and an oracle property for the variable selection method. Finally, a real data application illustrates the good performance of our procedure. | es_ES |
| dc.description.sponsorship | Ministerio de Economía y Competitividad; MTM2014-52876-R | es_ES |
| dc.description.sponsorship | Ministerio de Economía y Competitividad; MTM2017-82724-R | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431G/01 2016-2019 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431C2016-015 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED481A-2018/191. | es_ES |
| dc.identifier.citation | Novo, S.; Aneiros, G.; Vieu, P. Sparse Semi-Functional Partial Linear Single-Index Regression. Proceedings 2018, 2, 1190. | es_ES |
| dc.identifier.doi | 10.3390/proceedings2181190 | |
| dc.identifier.issn | 2504-3900 | |
| dc.identifier.uri | http://hdl.handle.net/2183/21126 | |
| dc.language.iso | eng | es_ES |
| dc.publisher | M D P I AG | es_ES |
| dc.relation.uri | https://doi.org/10.3390/proceedings2181190 | es_ES |
| dc.rights | Atribución 3.0 España | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
| dc.subject | Functional data analysis | es_ES |
| dc.subject | Variable selection | es_ES |
| dc.subject | Sparse model | es_ES |
| dc.subject | Dimension reduction | es_ES |
| dc.subject | Functional single-index model | es_ES |
| dc.subject | Semiparametric model | es_ES |
| dc.title | Sparse Semi-Functional Partial Linear Single-Index Regression | es_ES |
| dc.type | conference output | es_ES |
| dspace.entity.type | Publication | |
| relation.isAuthorOfPublication | bed2d73f-eff0-4e47-a3fb-27f0cfce07d9 | |
| relation.isAuthorOfPublication | 449cae44-40ef-41ac-994a-834bd5a05b2f | |
| relation.isAuthorOfPublication.latestForDiscovery | bed2d73f-eff0-4e47-a3fb-27f0cfce07d9 |
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