LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quad...LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quadratic loss function in most of classical credibility models. The Bayes premium and the credibility premium are derived under LINEX loss function. The consistency of Bayes premium and credibility premium were also checked. Finally, the simulation was introduced to show the differences between the credibility estimator we derived and the classical one.展开更多
针对应用RBF(Radial Basis Function)神经网络信用评分中存在的第Ⅰ类错误率高的问题,提出了基于Linex损失下RBF神经网络分类方法,并给出了UCI(University of California Irvine)中德国信用评分数据集上的测试结果。实验结果表明,该方...针对应用RBF(Radial Basis Function)神经网络信用评分中存在的第Ⅰ类错误率高的问题,提出了基于Linex损失下RBF神经网络分类方法,并给出了UCI(University of California Irvine)中德国信用评分数据集上的测试结果。实验结果表明,该方法能有效解决传统RBF神经网络信用评分中存在的问题。展开更多
基金Supported by the NNSF of China(71001046)Supported by the NSF of Jiangxi Province(20114BAB211004)
文摘LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quadratic loss function in most of classical credibility models. The Bayes premium and the credibility premium are derived under LINEX loss function. The consistency of Bayes premium and credibility premium were also checked. Finally, the simulation was introduced to show the differences between the credibility estimator we derived and the classical one.
文摘针对应用RBF(Radial Basis Function)神经网络信用评分中存在的第Ⅰ类错误率高的问题,提出了基于Linex损失下RBF神经网络分类方法,并给出了UCI(University of California Irvine)中德国信用评分数据集上的测试结果。实验结果表明,该方法能有效解决传统RBF神经网络信用评分中存在的问题。
