Cargando…
Validation of PARX Models for Default Count Prediction
The growing importance of financial technology platforms, based on interconnectedness, makes necessary the development of credit risk measurement models that properly take contagion into account. Evaluating the predictive accuracy of these models is achieving increasing importance to safeguard inves...
Autores principales: | Agosto, Arianna, Raffinetti, Emanuela |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861314/ https://www.ncbi.nlm.nih.gov/pubmed/33733098 http://dx.doi.org/10.3389/frai.2019.00009 |
Ejemplares similares
-
Editorial: Explainable artificial intelligence models and methods in finance and healthcare
por: Caffo, Brian S., et al.
Publicado: (2022) -
Corrigendum: Editorial: Explainable artificial intelligence models and methods in finance and healthcare
por: Caffo, Brian S., et al.
Publicado: (2023) -
Spatial Regression Models to Improve P2P Credit Risk Management
por: Agosto, Arianna, et al.
Publicado: (2019) -
On the Improvement of Default Forecast Through Textual Analysis
por: Cerchiello, Paola, et al.
Publicado: (2020) -
Effects of data count and image scaling on Deep Learning training
por: Hirahara, Daisuke, et al.
Publicado: (2020)