Comparação do desempenho de Modelos Lineares Generalizados (MLG) e Modelos Aditivos Generalizados (MAG) na predição de dados ﬁnanceiros em credit score
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This study aimed to present and compare the performance of two different methodologies for statistical modeling of ﬁnancial data with dichotomous response, speciﬁcally exempliﬁed by models of credit score as well as methodologies for validation and performance analysis of these models. One of the measures used in this analysis is the lift, often used in marketing, but little used in the ﬁnancial area, this measure is also used as a descriptive technique for categorizing variables. The techniques presented here are the Generalized Linear Models (GLM), the most usual method, and Generalized Additive Models (GAM), unusual in ﬁnance because it is a semi-parametric or nonparametric model, generating even some diﬃculty in interpretation because it does not present parameters. The predictive capabilities of the two techniques are compared in an application on real data and in a simulation study.