Sparse nonparametric model for regression with functional covariate

G. Aneiros and P. Vieu

Journal of Nonparametric Statistics, 2016, vol. 28, issue 4, 839-859

Abstract: This paper proposes a fully nonparametric model for regression problems involving an infinite-dimensional covariate in which sparsity is modelled in an additive way. The continuous nature of the variable allows to develop new variable selection procedures. Theoretical results show the improvement, in terms of both rate of convergence and number $ p_n $ pn of predictor variables in the model, that one can get from these approaches. An application to some real curves data set is finally presented, which illustrates the double practical interest of the method: good predictive behaviour and interpretability of the outputs as points of the curves being of most impact.

Date: 2016
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DOI: 10.1080/10485252.2016.1234050

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