LARGE-SCALE INFERENCE WITH BLOCK STRUCTURE

• 成果类型: Article
• 署名作者: Kou, Jiyao; Walther, Guenther
• 署名单位: Stanford University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/21-AOS2162
• 发表日期: 2022
• 页码: 1541-1572
• 关键词: likelihood ratio test; HIGHER CRITICISM; CLASSIFICATION; tests

摘要

The detection of weak and rare effects in large amounts of data arises in a number of modern data analysis problems. Known results show that in this situation the potential of statistical inference is severely limited by the large-scale multiple testing that is inherent in these problems. Here, we show that fundamentally more powerful statistical inference is possible when there is some structure in the signal that can be exploited, for example, if the signal is clustered in many small blocks, as is the case in some relevant applications. We derive the detection boundary in such a situation where we allow both the number of blocks and the block length to grow polynomially with sample size. We derive these results both for the univariate and the multivariate settings as well as for the problem of detecting clusters in a network. These results recover as special cases the sparse signal detection problem (Ann. Statist. 32 (2004) 962-994) where there is no structure in the signal, as well as the scan problem (Statist. Sinica 23 (2013) 409-428) where the signal comprises a single interval. We develop methodology that allows optimal adaptive detection in the general setting, thus exploiting the structure if it is present without incurring a relevant penalty in the case where there is no structure. The advantage of this methodology can be considerable, as in the case of no structure the means need to increase at the rate root log n to ensure detection, while the presence of structure allows detection even if the means decrease at a polynomial rate.