Adaptive Sparse Estimation With Side Information

• 成果类型: Article
• 署名作者: Banerjee, Trambak; Mukherjee, Gourab; Sun, Wenguang
• 署名单位: University of Southern California
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1679639
• 发表日期: 2020
• 页码: 2053-2067
• 关键词: minimax estimation; EMPIRICAL BAYES; DISCOVERY; vector

摘要

The article considers the problem of estimating a high-dimensional sparse parameter in the presence of side information that encodes the sparsity structure. We develop a general framework that involves first using an auxiliary sequence to capture the side information, and then incorporating the auxiliary sequence in inference to reduce the estimation risk. The proposed method, which carries out adaptive Stein's unbiased risk estimate-thresholding using side information (ASUS), is shown to have robust performance and enjoy optimality properties. We develop new theories to characterize regimes in which ASUS far outperforms competitive shrinkage estimators, and establish precise conditions under which ASUS is asymptotically optimal. Simulation studies are conducted to show that ASUS substantially improves the performance of existing methods in many settings. The methodology is applied for analysis of data from single cell virology studies and microarray time course experiments. for this article are available online.