Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Sullivan Hué"'
In credit scoring, machine learning models are known to outperform standard parametric models. As they condition access to credit, banking supervisors and internal model validation teams need to monitor their predictive performance and to identify th
Externí odkaz:
http://arxiv.org/abs/2212.05866
Publikováno v:
European Journal of Operational Research
European Journal of Operational Research, Elsevier, 2022, 297 (3), pp.1178-1192. ⟨10.1016/j.ejor.2021.06.053⟩
European Journal of Operational Research, Elsevier, 2022, 297 (3), pp.1178-1192
European Journal of Operational Research, Elsevier, 2022, 297 (3), pp.1178-1192. ⟨10.1016/j.ejor.2021.06.053⟩
European Journal of Operational Research, Elsevier, 2022, 297 (3), pp.1178-1192
In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in th
Autor:
Jérémy Leymarie, Sullivan Hué
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
Journal of Economic Dynamics and Control
Journal of Economic Dynamics and Control, Elsevier, 2019, 100, pp.86-114. ⟨10.1016/j.jedc.2018.12.001⟩
Journal of Economic Dynamics and Control, Elsevier, 2019, 100, pp.86-114. ⟨10.1016/j.jedc.2018.12.001⟩
Granger-causality measures of interconnectedness between financial institutions are useful indicators of systemic risk (Billio et al., 2012) [Journal of Financial Economics], as they help in evaluating how far the distress of one institution is disse
In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::19f38ae622c42784af02f693b6fd19d8
https://hal.science/hal-02507499v3
https://hal.science/hal-02507499v3