Predicting stillbirth using LASSO with structured penalties

Abstract

Using a structured fusion penalty in regression models containing only categorical explanatory variables yields patterns of indicator variables that are simpler and more easily interpretable than regressions produced using the more commonly-used LASSO and group LASSO penalties. We construct logistic regression models for predicting stillbirth from categorical explanatory variables and demonstrate that using a structured fusion penalty produces regressions that are easier to interpret yet yield similar predictive ability.