GAP: A General Framework for Information Pooling in Two-Sample Sparse Inference

DOI10.1080/01621459.2019.1611585zbMath1441.62195OpenAlexW2943765507WikidataQ127931313 ScholiaQ127931313MaRDI QIDQ5120661

T. Tony Cai, Yin Xia, Wenguang Sun

Publication date: 15 September 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2019.1611585

zbMATH Keywords

false discovery ratedependent testsadjusted \(p\)-valuemultiple testing with groupscovariate-assisted inferenceuncorrelated screening

Mathematics Subject Classification ID

Paired and multiple comparisons; multiple testing (62J15)

Related Items

LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing, Heteroscedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing, Structure–Adaptive Sequential Testing for Online False Discovery Rate Control, False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation, Two-sample testing of high-dimensional linear regression coefficients via complementary sketching

Uses Software

• DNA
• HdBCS

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