Novo Díaz, SilviaAneiros, GermánVieu, Philippe2018-10-092018-10-092018-09-17Novo, S.; Aneiros, G.; Vieu, P. Sparse Semi-Functional Partial Linear Single-Index Regression. Proceedings 2018, 2, 1190.2504-3900http://hdl.handle.net/2183/21126Trátase dun resumo estendido da ponencia[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.engAtribución 3.0 Españahttp://creativecommons.org/licenses/by/3.0/es/Functional data analysisVariable selectionSparse modelDimension reductionFunctional single-index modelSemiparametric modelconference outputopen access10.3390/proceedings2181190