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A Model-Free Screening Selection Approach by Local Derivative Estimation

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Francesco Giordano

    (University of Salerno)

  • Sara Milito

    (University of Salerno)

  • Maria Lucia Parrella

    (University of Salerno)

Abstract

A new model-free screening method, called Derivative Empirical Likelihood Independent Screening (D-ELSIS) is proposed for high-dimensional regression analysis. Without requiring a specific parametric form of the underlying data model, our method is able to identify explanatory variables that contribute to the explanation of the response variable in nonparametric and non-additive contexts. This approach is fully nonparametric and combines the estimation of marginal derivatives by the local polynomial estimator together with the empirical likelihood technique. The proposed method can be applied to variable screening problems emerging from a wide range of areas, from genomic and health science to economics, finance and machine learning. We report some simulation results in order to show that the D-ELSIS screening approach performs satisfactorily.

Suggested Citation

  • Francesco Giordano & Sara Milito & Maria Lucia Parrella, 2021. "A Model-Free Screening Selection Approach by Local Derivative Estimation," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 243-250, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_36
    DOI: 10.1007/978-3-030-78965-7_36
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    Cited by:

    1. Giordano, Francesco & Milito, Sara & Parrella, Maria Lucia, 2023. "Linear and nonlinear effects explaining the risk of Covid-19 infection: an empirical analysis on real data from the USA," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).

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