Cure events in default prediction
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DOI: 10.1016/j.ejor.2014.04.046
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- Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
- Peter-Hendrik Ingermann & Frederik Hesse & Christian Bélorgey & Andreas Pfingsten, 2016. "The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 179-228, August.
- Yuting Li & Tong Chen & Baogui Xin, 2016. "Optimal Financing Decisions of Two Cash-Constrained Supply Chains with Complementary Products," Sustainability, MDPI, vol. 8(5), pages 1-17, April.
- Perko, Igor, 2017. "Behaviour-based short-term invoice probability of default evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 1045-1054.
- Wanying Song & Jian Min & Jianbo Yang, 2023. "Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide- ℓ p Penalty and Deep Learning Approach," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
- Yang, Qi & He, Haijin & Lu, Bin & Song, Xinyuan, 2022. "Mixture additive hazards cure model with latent variables: Application to corporate default data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
- Jianjun Yu & Dan Zhu, 2018. "Study on the Selection Strategy of Supply Chain Financing Modes Based on the Retailer’s Trade Grade," Sustainability, MDPI, vol. 10(9), pages 1-12, August.
- Lohmann, Christian & Ohliger, Thorsten, 2024. "Predicting the cure of a defaulted company: Nonlinear relationships between loan-related variables and the cure probability," Research in International Business and Finance, Elsevier, vol. 70(PB).
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