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Assessing the Importance of Risk Factors in Distance-Based Generalized Linear Models

Author

Listed:
  • Eva Boj

    (Universitat de Barcelona)

  • Teresa Costa

    (Universitat de Barcelona)

  • Josep Fortiana

    (Universitat de Barcelona)

  • Anna Esteve

    (Hospital Universitari Germans Trias i Pujol, CIBER Epidemiologia i Salut P’ublica (IBERESP))

Abstract

Predictions with distance-based linear and generalized linear models rely upon latent variables derived from the distance function. This key feature has the drawback of adding a non-linearity layer between observed predictors and response which shields one from the other and, in particular, prevents us from interpreting linear predictor coefficients as influence measures. In actuarial applications such as credit scoring or a priori rate-making we cannot forgo this capability, crucial to assess the relative leverage of risk factors. Towards the goal of recovering this functionality we define and study influence coefficients, measuring the relative importance of observed predictors. Unavoidably, due to inherent model non-linearities, these quantities will be local -valid in a neighborhood of a given point in predictor space.

Suggested Citation

  • Eva Boj & Teresa Costa & Josep Fortiana & Anna Esteve, 2015. "Assessing the Importance of Risk Factors in Distance-Based Generalized Linear Models," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 951-962, December.
    • Handle: RePEc:spr:metcap:v:17:y:2015:i:4:d:10.1007_s11009-014-9415-6
    DOI: 10.1007/s11009-014-9415-6
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    References listed on IDEAS

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