Hierarchical recognition of sparse patterns in large-scale simultaneous inference

• 成果类型: Article
• 署名作者: Sun, Wenguang; Wei, Zhi
• 署名单位: University of Southern California; New Jersey Institute of Technology
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asv012
• 发表日期: 2015
• 页码: 267280
• 关键词: false discovery rate; MULTIPLE TEST PROCEDURES; tests; CLASSIFICATION; DESIGN; error; rates

摘要

We study how to separate signals from noisy data accurately and determine the patterns of the selected signals. Controlling the inflation of false positive errors is important in large-scale simultaneous inference but has not been addressed in the pattern recognition literature. We develop a decision-theoretic framework and formulate the sparse pattern recognition problem as a simultaneous inference problem with multiple decision trees. Oracle and adaptive classifiers are proposed for maximizing the expected number of true positives subject to a constraint on the overall false positive rate. Existing results on multiple testing are extended by allowing more than two states of nature, hierarchical decision-making and new error rate concepts.