Métodos de categorização de variáveis preditoras em modelos de regressão para variáveis binárias
Fecha
2017-06-13Autor
Silva, Diego Mattozo Bernardes da
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Regression models for binary response variables are very common in several areas of knowledge.
The most used model in these situations is the logistic regression model, which assumes that the
logit of the probability of a certain event is a linear function of the predictors variables. When
this assumption is not reasonable, it is common to make some changes in the model, such as:
transformation of predictor variables and/or add quadratic or cubic terms to the model. The problem
with this approach is that it hinders parameter interpretation, and in some areas it is fundamental to
interpret the parameters. Thus, a common approach is to categorize the quantitative covariates. This
work aims to propose two new classes of categorization methods for continuous variables in binary
regression models. The first class of methods is univariate and seeks to maximize the association
between the response variable and the categorized covariate using measures of association for
qualitative variables. The second class of methods is multivariate and incorporates the predictor
variables correlation structure through the joint categorization of all covariates. To evaluate the
performance, we applied the proposed methods and four existing categorization methods in 3 credit
scoring databases and in two simulated cenarios. The results in the real databases suggest that the
proposed univariate class of categorization methods performs better than the existing methods when
we compare the predictive power of the logistic regression model. The results in the simulated
databases suggest that both proposed classes perform better than the existing methods. Regarding
computational performance, the multivariate method is inferior and the univariate method is superior
to the existing methods.