Hierarchical recognition of sparse patterns in large-scale simultaneous inference
研究如何从噪声数据中准确分离信号并识别其模式，提出决策理论框架，将稀疏模式识别转化为多决策树的同时推断问题，在控制整体误报率下最大化真阳性数量。
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.