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.
Osmetti, S. A., Calabrese, R., A GENERALIZED ADDITIVE MODEL FOR BINARY RAREEVENTS DATA: AN APPLICATION TO CREDIT DEFAULTS, in Analysis and Modeling of Complex Data in Behavioural and Social Sciences In Book of short papers JCS, (Anacapri, 03-04 September 2012), Cleup, Padova 2012: 1-4 [http://hdl.handle.net/10807/31688]
A GENERALIZED ADDITIVE MODEL FOR BINARY RARE EVENTS DATA: AN APPLICATION TO CREDIT DEFAULTS
Osmetti, Silvia Angela;
2012
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.
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