Variable Selection and Computation of the Prior Probability of a Model via ROC Curves Methodology
Christos Koukouvinos, Christina Parpoula · Journal of Data Science · 2021
Nowadays, extensive amounts of data are stored which require the development of specialized methods for data analysis in an understandable way. In medical data analysis many potential factors are usually intro- duced to determine an outcome response variable. The main objective of variable selection is enhancing the prediction performance of the predictor variables and identifying correctly and parsimoniously the faster and more cost-eective predictors that have an important inuence on the response. Various variable selection techniques are used to improve predictability and obtain the \best model derived from a screening procedure. In our study, we propose a variable subset selection method which extends to the classi- cation case the idea of selecting variables and combines a nonparametric criterion with a likelihood based criterion. In this work, the Area Under the ROC Curve (AUC) criterion is used from another viewpoint in order to determine more directly the important factors. The proposed method re- vealed a modication ( BIC) of the modied Bayesian Information Criterion (mBIC). The comparison of the introduced BIC to existing variable selec- tion methods is performed by some simulating experiments and the Type I and Type II error rates are calculated. Additionally, the proposed method is applied successfully to a high-dimensional Trauma data analysis, and its good predictive properties are conrmed.