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

• Silvia Novo ^[1] ; Germán Aneiros ^[1] ; Philippe Vieu ^[2]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
  2. [2] Paul Sabatier University

     Paul Sabatier University

     Arrondissement de Toulouse, Francia

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 30, Nº. 2, 2021, págs. 481-504
• Idioma: inglés
• DOI: 10.1007/s11749-020-00728-w
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.