An optimal design framework for lasso sign recovery

• 成果类型: Article; Early Access
• 署名作者: Stallrich, Jonathan W.; Young, Kade; Weese, Maria L.; Smucker, Byran J.; Edwards, David J.
• 署名单位: North Carolina State University; University System of Ohio; Miami University; University System of Ohio; Miami University; Henry Ford Health System; Henry Ford Health System; Michigan State University; Michigan State University College of Human Medicine; Virginia Commonwealth University; Citadel Military College South Carolina
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkaf026
• 发表日期: 2025
• 关键词: coordinate-exchange algorithm; Model Selection Consistency; supersaturated designs; DANTZIG SELECTOR; CONSTRUCTION; criterion; sparsity

摘要

Supersaturated designs investigate more factors than there are runs and are often constructed under a criterion measuring a design's proximity to an unattainable orthogonal design. The most popular analysis identifies active factors by inspecting the solution path of a penalized estimator, such as the lasso. Recent criteria encouraging positive correlations between factors have been shown to produce designs with more definitive solution paths so long as the active factors have positive effects. Two open problems affecting the understanding and practicality of supersaturated designs are: (1) do optimal designs under existing criteria maximize support recovery probability across an estimator's solution path and (2) why do designs with positively correlated columns produce more definitive solution paths when the active factors have positive sign effects? To answer these questions, we develop criteria maximizing the lasso's sign recovery probability. We prove that an orthogonal design is an ideal structure when the signs of the active factors are unknown, and a design with constant, small, positive correlations is ideal when the signs are assumed known. A computationally efficient design search algorithm is proposed that first filters through optimal designs under new heuristic criteria to select the one that maximizes the lasso sign recovery probability.