We propose a hybrid procedure that combines Morris' elementary effect, bootstrap-based hypothesis testing, and aggregated false discovery rate control to simultaneously identify the main and interaction effects in a statistical model. Numerical experiments demonstrate the efficiency and efficacy of our method.
['Wen Shi', 'Jennifer Shang', 'Zhang Zhigang']
Simulation screening and false discovery rate control for both main and interaction effects