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

Silvia Angela Osmetti, Raffaella Calabrese

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationAnalysis and Modeling of Complex Data in Behavioural and Social Sciences In Book of short papers JCS
Pages1-4
Number of pages4
Publication statusPublished - 2012
EventJCS - CLADAG 2012 - Anacapri
Duration: 3 Sep 20124 Sep 2012

Conference

ConferenceJCS - CLADAG 2012
CityAnacapri
Period3/9/124/9/12

Keywords

  • CREDIT DEFAULT
  • GENERALIZED ADDITIVE MODEL

Fingerprint

Dive into the research topics of 'A GENERALIZED ADDITIVE MODEL FOR BINARY RARE EVENTS DATA: AN APPLICATION TO CREDIT DEFAULTS'. Together they form a unique fingerprint.

Cite this