Frequent-voting Independence Screening for Data of Different Types or Different Dimensions

Modern datasets often include different types of variables with complex features, making variable selection particularly challenging.For example, a measure of dependence with the response variable may not be directly comparable among predictor variables of different types and different dimensions.To address this challenge, this work proposes a frequent-voting based independent screening method for variable selection, which avoids a direct comparison of the dependence measure among different variables.Asymptotic analyses show that the proposed method selects all of the active variables with probability converging to one.We also demonstrate its great finite sample performance through numerical experiments and the application to an ADHD study.