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首页> 外文期刊>Pesquisa Operacional >CREDIT SCORING MODELING WITH STATE-DEPENDENT SAMPLE SELECTION: A COMPARISON STUDY WITH THE USUAL LOGISTIC MODELING
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CREDIT SCORING MODELING WITH STATE-DEPENDENT SAMPLE SELECTION: A COMPARISON STUDY WITH THE USUAL LOGISTIC MODELING

机译：与状态相关的样本选择的信用评分模型：与常规逻辑模型的比较研究

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摘要

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model, a logistic regression with state-dependent sample selection model and a bounded logistic regression model via a large simulation study. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian retail bank portfolio. Our simulation results so far revealed that there is nostatistically significant difference in terms of predictive capacity among the naive logistic regression models, the logistic regression with state-dependent sample selection models and the bounded logistic regression models. However, there is difference between the distributions of the estimated default probabilities from these three statistical modeling techniques, with the naive logistic regression models and the boundedlogistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. Which are common in practice.
机译：统计方法已广泛用于评估信用评分分类模型的功能，以减少在向客户授予信用便利时做出错误决定的风险。可以基于诸如敏感性，特异性，预测值，准确性，相关系数和信息理论度量（例如相对熵和互信息）之类的度量来评估分类模型的预测质量。在本文中，我们通过大型仿真分析了朴素逻辑回归模型，具有状态依赖样本选择模型的逻辑回归和有界逻辑回归模型的性能。此外，作为案例研究，该方法在从巴西零售银行投资组合中提取的数据集上进行了说明。到目前为止，我们的模拟结果表明，在幼稚的逻辑回归模型，具有状态依赖样本选择模型的逻辑回归和有界逻辑回归模型之间，在预测能力方面没有统计学上的显着差异。但是，这三种统计建模技术估计的默认概率分布之间存在差异，其中幼稚的逻辑回归模型和有界逻辑回归模型始终低估了这种概率，尤其是在存在平衡样本的情况下。在实践中很常见。

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