A Generalized Additive Model for Binary Rare Events Data: an Application to Credit Defaults.

Silvia Angela Osmetti, Raffaella Calabrese

Risultato della ricerca: Contributo in libroChapter

1 Citazioni (Scopus)

Abstract

We aim at proposing a new model for binary rare events, i.e. binary depen- dent variable with a very small number of ones.We extend the Generalized Extreme Value (GEV) regression model proposed by Calabrese and Osmetti [5] to a Gener- alized Additive Model (GAM). We suggest to consider the quantile function of the GEV distribution as a link function in a GAM, so we propose the Generalized Extreme Value Additive (GEVA) model. In order to estimate the GEVA model, a modified version of the local scoring algorithm of GAM is proposed. Finally, to model default probability, we apply our proposal to empirical data on Italian Small and Medium Enterprises (SMEs). The results show that the GEVA model has a higher predictive accuracy to identify the rare event than the logistic additive model.
Lingua originaleEnglish
Titolo della pubblicazione ospiteAnalysis and Modeling of Complex Data in Behavioural and Social Sciences
EditorD Vicari, A Okada, G Ragozini, C Weihs
Pagine73-81
Numero di pagine9
DOI
Stato di pubblicazionePubblicato - 2014

Serie di pubblicazioni

NomeSTUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION

Keywords

  • credit defaults
  • generalized additive model
  • generalized extreme value distribution
  • local scoring algorithm

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