Two-stage logistic regression model

Authors:
Mijung Kim
Affiliations:
Institute for Mathematical Sciences, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-752, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

In this article, a logistic regression model combined with decision tree for dealing with a significant interaction effect among the explanatory variables is suggested. Decision tree is applied for investigating the interaction among explanatory variables and grouping subjects based on @g^2 value for optimal split. Each group of subjects which is named cluster is determined by optimal split for the interacting explanatory variables. The suggested model incorporates this cluster as an explanatory variable for including significant interaction in the logistic regression model. This model shows better performances in assessment of predictive model than the logistic regression model or decision tree: better ranked classes, increased correct classification rate and R^2, improved Kolmogorov-Smirnov (K-S) statistic, and a better lift. National pension data are applied to this model, and as an application of the suggested model, strategies for reducing financial risks in managing and planning for pension financing are illustrated.