Promoting Variable Effect Consistency in Mixture Cure Model for Credit Scoring

Abstract

Mixture cure models are widely adopted in credit scoring. Mixture cure models consist of two parts: an incident part which predicts the probability of default and a latency part which predicts when they are likely to default. The two model parts describe two quite relevant credit aspects. So, it is reasonable to expect that the two sets of the coefficients are somewhat related. Moreover, in practical cases, it is difficult to interpret the results when the two sets of the coefficients of the same variables have conflicting signs. Most existing works either ignore the interconnections of the two sets of coefficients or impose a strict constraint between them. We proposed a mixture cure model considering the variable effect consistency using a sign-based penalty. It is a more flexible model that allows the two sets of coefficients to be in different distributions and magnitudes. To accommodate high-dimensional credit data, a group lasso penalty is also imposed for variable selection. Simulation shows that the proposed method has competitive performance compared with alternative methods in terms of estimation and prediction. Furthermore, the empirical study illustrates that the proposed method outperforms the alternative method and can improve the interpretability of the results.