An Efficient Variable Selection Approach for Analyzing Designed Experiments

The analysis of experiments in which numerous potential variables are examined is driven by the principles of effect sparsity, effect hierarchy, and effect heredity. We propose an efficient variable selection strategy to specifically address the unique challenges faced by such analysis. The proposed methods are natural extensions of the LARS general-purpose variable selection algorithm. They can be computed very rapidly and can find sparse models that better satisfy the goals of experiments. Simulations and real examples are used to illustrate the wide applicability of the proposed methods.