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Ranking-Based Variable Selection for the Default Risk of Bank Loan Holders

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

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  • Francesco Giordano

    (Università degli Studi di Salerno, Dipartimento di Scienze Economiche e Statistiche)

  • Marcella Niglio

    (Università degli Studi di Salerno, Dipartimento di Scienze Economiche e Statistiche)

  • Marialuisa Restaino

    (Università degli Studi di Salerno, Dipartimento di Scienze Economiche e Statistiche)

Abstract

In this paper we extend the Ranking Based Variable Selection technique (Baranowsky et al., Statistica Sinica 30, 1485–1516 (2020)) to the framework of general linear regression models. After the presentation of the main steps of the algorithm, it is applied to select the variables affecting the repayment ability of bank loan holders. We give evidence that, unlike some largely applied selection methods, the algorithm is robust to the presence of high correlated variables and the number of features selected does not change even when the dataset is contaminated with irrelevant artificial covariates.

Suggested Citation

  • Francesco Giordano & Marcella Niglio & Marialuisa Restaino, 2022. "Ranking-Based Variable Selection for the Default Risk of Bank Loan Holders," Springer Books, in: Marco Corazza & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 309-314, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-99638-3_50
    DOI: 10.1007/978-3-030-99638-3_50
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