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Screening Covariates in Presence of Unbalanced Binary Dependent Variable

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Francesco Giordano

    (University of Salerno)

  • Marcella Niglio

    (University of Salerno)

  • Marialuisa Restaino

    (University of Salerno)

Abstract

In this contribution we propose a new method to identify the most relevant covariates in a large dataset that (i) is applicable in presence of regression models where the binary dependent variable is characterized by a very small number of ones than zeros, (ii) is not strongly influenced by the correlation between covariates, (iii) is easily applied when the number of predictors increases up to infinity and/or it is greater than the sample size. The proposed procedure extends the idea of Sure Independence Screening for the linear regression model to the Generalized Extreme Value regression framework. This technique allows to define a set of relevant covariates that survive after applying the screening procedure and that, with a probability tending to one, includes the true relevant covariates. We validate the proposed procedure by a simulation study and an empirical analysis devoted to the prediction of firms failure.

Suggested Citation

  • Francesco Giordano & Marcella Niglio & Marialuisa Restaino, 2021. "Screening Covariates in Presence of Unbalanced Binary Dependent Variable," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 257-263, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_38
    DOI: 10.1007/978-3-030-78965-7_38
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