The purpose of this paper is to apply count data models to predict the number of times a borrower pays late the amount of the credit. Poisson models and negative binomial distribution models, taking into account the observed heterogeneity, are generally used in situations where the dependent variable is discrete. Alternatively, we propose to use non parametric model where the relationship form between conditional mean and the explanatory variables is unknown. The empirical results found suggest that the nonparametric poisson model regression has the best prediction of the number of default payment.

中文翻译：

---

使用非参数计数模型进行信用评分

本文的目的是应用计数数据模型来预测借款人延迟偿还信用额度的次数。考虑到所观察到的异质性，通常在因变量是离散的情况下使用泊松模型和负二项分布模型。另外，我们建议使用非参数模型，其中条件均值和解释变量之间的关系形式未知。发现的经验结果表明，非参数泊松模型回归可以最好地预测违约金的数量。