Sparse semiparametric regression when predictors are mixture of functional and high-dimensional variables

Silvia Novo (), Germán Aneiros and Philippe Vieu
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Silvia Novo: Universidade da Coruña, Centro de investigación CITIC
Germán Aneiros: Universidade da Coruña, Centro de investigación CITIC
Philippe Vieu: Université Paul Sabatier, IMT

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 30, issue 2, No 11, 504 pages

Abstract: Abstract This paper aims to front with dimensionality reduction in regression setting when the predictors are a mixture of functional variable and high-dimensional vector. A flexible model, combining both sparse linear ideas together with semiparametrics, is proposed. A wide scope of asymptotic results is provided: this covers as well rates of convergence of the estimators as asymptotic behaviour of the variable selection procedure. Practical issues are analysed through finite sample-simulated experiments, while an application to Tecator’s data illustrates the usefulness of our methodology.

Keywords: Functional data analysis; Big data analysis; Variable selection; Sparse model; Dimension reduction; Functional single-index model; Semiparametrics; 62G05; 62G08; 62G20 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11749-020-00728-w

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