Fast Algorithm for Impact Point Selection in Semiparametric Functional Models
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Fast Algorithm for Impact Point Selection in Semiparametric Functional ModelsFecha
2019-07-31Cita bibliográfica
NOVO, Silvia; ANEIROS, Germán; VIEU, Philippe. Fast Algorithm for Impact Point Selection in Semiparametric Functional Models. En Multidisciplinary Digital Publishing Institute Proceedings. 2019. p. 14.
Resumen
[Abstract] A new sparse semiparametric functional model is proposed, which tries to incorporate the influence of two functional variables in a scalar response in a quite simple and interpretable way. One of the functional variables is included trough a single-index structure and the other one linearly, but trough the high-dimensional vector of its discretized observations. For this model, a new algorithm for impact point selection in the linear part and for the model estimation is proposed. This procedure is based on the functional origin of the linear covariates. Some asymptotic results will ensure the good performance of the method. The computational efficiency of the algorithm, without loss of predictive power, will be showed trough a simulation study and a real data application, by comparing its results with those obtained trough the standard PLS method.
Palabras clave
Functional data analysis
Multi-functional covariates
Dimension reduction
Variable selection
Functional single-index model
Semiparametric model
Multi-functional covariates
Dimension reduction
Variable selection
Functional single-index model
Semiparametric model
Versión del editor
Derechos
Atribución 4.0 España
ISSN
2504-3900