The ROPE framework for model selection in linear regression
Many methods for model selection fall under the “Loss + Penalty” approach, where the “Loss” prizes the model for fitting the data well and the “Penalty” penalizes the model for its complexity. One method in particular (the Fence method) uses a data adaptive bootstrapping algorithm, the Adaptive Fence (AF), to select the penalty. The goal of the AF algorithm is to estimate the optimal penalty, i.e., the penalty that maximize the probability of selecting the true data generating model, which is a common desired objective for model selection. We show how the AF approach for estimating the optimal penalty can be improved and propose a new algorithm that can provide a better estimate, which we coin the Resampling-based Optimal Penalty Estimate (ROPE) algorithm. We show how to implement both algorithms to select the penalty multiplier in the Generalized Information Criteria (GIC) for model selection in linear regression. Then, we carry out a simulation study to compare the two algorithms. The results of the study indicate that the ROPE algorithm outperforms the AF algorithm in our simulation settings.