Calabrese, Raffaella and Osmetti, Silvia Angela (2014) A Generalized Additive Model for Binary Rare Events Data: An Application to Credit Defaults. In: Studies in Classification, Data Analysis, and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization . Springer International Publishing, Switzerland, pp. 73-81. ISBN 9783319066912. Official URL: https://doi.org/10.1007/978-3-319-06692-9_9
Calabrese, Raffaella and Osmetti, Silvia Angela (2014) A Generalized Additive Model for Binary Rare Events Data: An Application to Credit Defaults. In: Studies in Classification, Data Analysis, and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization . Springer International Publishing, Switzerland, pp. 73-81. ISBN 9783319066912. Official URL: https://doi.org/10.1007/978-3-319-06692-9_9
Calabrese, Raffaella and Osmetti, Silvia Angela (2014) A Generalized Additive Model for Binary Rare Events Data: An Application to Credit Defaults. In: Studies in Classification, Data Analysis, and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization . Springer International Publishing, Switzerland, pp. 73-81. ISBN 9783319066912. Official URL: https://doi.org/10.1007/978-3-319-06692-9_9
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 proposed by Calabrese and Osmetti (Journal of Applied Statistics 40(6):1172–1188, 2013) 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). The results show that the GEVA model has a higher predictive accuracy to identify the rare event than the logistic additive model.
| Item Type: | Book Section |
|---|---|
| Uncontrolled Keywords: | Generalized additive model; Generalized extreme value distribution; Rare event |
| Subjects: | H Social Sciences > HG Finance |
| Divisions: | Faculty of Social Sciences > Essex Business School Faculty of Social Sciences > Essex Business School > Essex Finance Centre |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 24 Oct 2014 14:48 |
| Last Modified: | 05 Dec 2024 12:07 |
| URI: | http://repository.essex.ac.uk/id/eprint/11184 |