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 originale | English |
|---|---|
| Titolo della pubblicazione ospite | Analysis and Modeling of Complex Data in Behavioural and Social Sciences In Book of short papers JCS |
| Pagine | 1-4 |
| Numero di pagine | 4 |
| Stato di pubblicazione | Pubblicato - 2012 |
| Evento | JCS - CLADAG 2012 - Anacapri Durata: 3 set 2012 → 4 set 2012 |
Convegno
| Convegno | JCS - CLADAG 2012 |
|---|---|
| Città | Anacapri |
| Periodo | 3/9/12 → 4/9/12 |
Keywords
- CREDIT DEFAULT
- GENERALIZED ADDITIVE MODEL
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