A GENERALIZED ADDITIVE MODEL FOR BINARY RARE EVENTS DATA: AN APPLICATION TO CREDIT DEFAULTS

Silvia Angela Osmetti, 27494, DI ECONOMIA FACOLTA', MILANO - Dipartimento di Scienze statistiche, Raffaella Calabrese, DI ECONOMIA FACOLTA'

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

We aim at proposing a new model for binary rare events, i.e. binary dependent variable with a very small number of ones. We extend the Generalized Extreme Value (GEV) regression model (Calabrese and Osmetti, 2011) to a Generalized 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). We obtain that the GEVA model shows a high predictive accuracy to identify the rare event.
Lingua originaleEnglish
Titolo della pubblicazione ospiteAnalysis and Modeling of Complex Data in Behavioural and Social Sciences In Book of short papers JCS
Pagine1-4
Numero di pagine4
Stato di pubblicazionePubblicato - 2012
EventoJCS - CLADAG 2012 - Anacapri
Durata: 3 set 20124 set 2012

Convegno

ConvegnoJCS - CLADAG 2012
CittàAnacapri
Periodo3/9/124/9/12

Keywords

  • CREDIT DEFAULT
  • GENERALIZED ADDITIVE MODEL

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