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Financial data modeling by Poisson mixture regression

机译：通过泊松混合回归进行财务数据建模

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In many financial applications, Poisson mixture regression models are commonly used to analyze heterogeneous count data. When fitting these models, the observed counts are supposed to come from two or more subpopulations and parameter estimation is typically performed by means of maximum likelihood via the Expectation-Maximization algorithm. In this study, we discuss briefly the procedure for fitting Poisson mixture regression models by means of maximum likelihood, the model selection and goodness-of-fit tests. These models are applied to a real data set for credit-scoring purposes. We aim to reveal the impact of demographic and financial variables in creating different groups of clients and to predict the group to which each client belongs, as well as his expected number of defaulted payments. The model's conclusions are very interesting, revealing that the population consists of three groups, contrasting with the traditional good versus bad categorization approach of the credit-scoring systems.
机译：在许多金融应用中，泊松混合回归模型通常用于分析异构计数数据。在拟合这些模型时，观察到的计数应该来自两个或多个子种群，并且通常通过Expectation-Maximization算法通过最大似然来执行参数估计。在这项研究中，我们简要讨论了通过最大似然，模型选择和拟合优度检验来拟合泊松混合回归模型的过程。将这些模型应用于真实数据集以进行信用评分。我们旨在揭示人口统计和财务变量对创建不同客户组的影响，并预测每个客户所属的组以及其预期的违约付款数量。该模型的结论非常有趣，揭示了人口由三组组成，这与信用评分系统的传统优劣分类方法形成了对比。

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