A Bayesian-Frequentist Inference Approach for Post-Double Selection on Imputed and Bootstrapped Datasets

This project develops a hybrid Bayesian-Frequentist inference framework for post-double selection in imputed and bootstrapped datasets. The method uses the Bayesian-Factor to address variable selection challenges in multiply imputed data. This project is a collaboration with C. Tarantola.