基于贝叶斯界定折叠法的小企业信用评分模型研究  

Small Business Credit Scoring Model Based on Bayesian Inference Using Bound and Collapse

作　　者：邓超[1] 胡梅梅[1,2] 曾文潮 

机构地区：[1]中南大学商学院,湖南长沙410083 [2]湖南农业大学商学院,湖南长沙410128

出　　处：《管理工程学报》2015年第4期162-170,共9页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(71173241);教育部新世纪优秀人才支持计划资助项目(CET-10-0830);教育部人文社科基金资助项目(12YJC790065)

摘　　要：本文针对小企业信用评分模型演化过程中出现的样本选择偏差问题,引入拒绝推论的思想,利用贝叶斯界定折叠法有效解决因样本有偏引起的小企业信用评分模型分类能力丧失问题,该方法避免了有偏样本抽样分析中出现的迭代问题和随机方法中出现的收敛问题,并提供一种可以降低数据集条件分布和边际分布预测成本的确定性分析方法。实证结果表明,贝叶斯界定折叠法在样本筛选率分别为20%和40%的假设下,对样本填补率和模型分类能力均有较大贡献,具有较强的稳健性,是在非随机数据缺失机制下解决样本选择偏差问题的有效途径。In small business credit scoring, sample selection bias is commonly referred to as ＂reject inference＂, where partial observations of delinquent variables are missing due to a credit screening process of the bank. Sample selection bias may lead to biased parameter estimation, thereby affecting the accuracy of model prediction and the effectiveness of credit decision. Therefore, improving the sample selection bias is the crucial content of current credit scoring model studies.A literature review leads to the conclusion that most solutions currently proposed for reject inference are not fully validated. In this paper we propose a new reject inference technique based on Bayesian inference using bound and collapse firstly suggested by Sebastiani and Ramoni（2000）. The intuition of this method is that it is possible to set a bound for possible estimates of missing data within an interval defined by some extreme distribution regardless of the missing data mechanisms. The complete set of data will provide constrains on the interval. When information about the missing data mechanism is available, it is encoded in a probabilistic model of non-response and used to select a single estimate. The second step of BC collapses the interval into a single value of missing data. By this method a randomly imputed datum will be generated to replace the missing datum so that samples with complete data can be prepared for the evolution of small business credit scoring models. Based on the 2003 National Surveys of Small Business Finances（NSSBF） datasets, we designed an experiment to test the power and efficiency of the proposed model. Firstly, we developed a credit scoring model to predict the probability of credit delinquency based on logistic regression and the first sample. By applying this credit scoring model to the second sample, we simulated a credit granting policy. A selected sample was obtained by applying a credit cutoff policy. Secondly, we simulated the process of how the small business credit scoring model loses

关 键 词：拒绝推论 贝叶斯界定折叠法 信用评分模型 小企业 

分 类 号：F832.42[经济管理—金融学]