In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
本研究基于Give Me Some Credit数据集,开发了一种融合WOE编码与Logistic回归的信用评分卡模型,旨在解决金融机构在信贷风险评估中的核心挑战。研究的主要贡献在于:提出了一种优化的特征离散化方法,通过WOE转换有效处理非线性关系并增...本研究基于Give Me Some Credit数据集,开发了一种融合WOE编码与Logistic回归的信用评分卡模型,旨在解决金融机构在信贷风险评估中的核心挑战。研究的主要贡献在于:提出了一种优化的特征离散化方法,通过WOE转换有效处理非线性关系并增强模型解释性;构建了包含KS统计量、PSI稳定性和多决策阈值的综合评估体系,显著提升了模型验证的全面性与业务适用性。实证结果表明,该模型在测试集上取得了0.85的AUC值和0.452的KS统计量,展现出优秀的风险区分能力,同时PSI指标验证了模型在不同群体间的稳定性。本研究的方法论框架不仅为信用风险评估提供了技术参考,其评估体系也可推广至其他金融风险预测场景。然而,研究在特征工程深度和模型对比广度方面仍存在改进空间,为后续研究指明了方向。展开更多
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
文摘本研究基于Give Me Some Credit数据集,开发了一种融合WOE编码与Logistic回归的信用评分卡模型,旨在解决金融机构在信贷风险评估中的核心挑战。研究的主要贡献在于:提出了一种优化的特征离散化方法,通过WOE转换有效处理非线性关系并增强模型解释性;构建了包含KS统计量、PSI稳定性和多决策阈值的综合评估体系,显著提升了模型验证的全面性与业务适用性。实证结果表明,该模型在测试集上取得了0.85的AUC值和0.452的KS统计量,展现出优秀的风险区分能力,同时PSI指标验证了模型在不同群体间的稳定性。本研究的方法论框架不仅为信用风险评估提供了技术参考,其评估体系也可推广至其他金融风险预测场景。然而,研究在特征工程深度和模型对比广度方面仍存在改进空间,为后续研究指明了方向。
