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中文摘要：
Lasso二元选择分位数回归是较为新颖的统计方法,一方面通过Lasso的变量选择功能,能够从众多的影响因素中识别出关键因素;另一方面通过分位数回归对各因素在不同分位点处的异质影响进行细致刻画,能够获得更多信息进而实现信用状况的准确评估,可望在信用评估领域发挥重要作用。基于Lasso二元选择分位数回归,建立评估模型并将其应用于中国上市公司的信用评估。通过数值模拟和实证研究,将其与基于Logit回归、Lasso-Logit回归和支持向量机的评估效果进行对比,发现前者不但具备良好的变量选择能力而且可以获得最佳的评估效果。
英文摘要：
Lasso binary quantile regression is a quite new statistical method,on the one hand,it can identify the key factors from vast influence factors through the Lasso variable selection function;on the other hand,it can implement accurate credit evaluation through quantile regression meticulously depicting the various factors＇ heterogeneous influence,expected to play an important role in the field of credit evaluation.In this paper,we establish an evaluation model based on Lasso binary quantile regression and apply it to the credit evaluation of listed company in China.We compare Lasso binary quantile regression model with Logit model,Lasso-Logit model and support vector machine in terms of evaluation performance through simulation studies and real data analysis,finding that the former not only has the good capability of variable selection but also can get the best evaluation performance.
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