A new approach to multiple testing of grouped hypotheses

DOI10.1016/j.jspi.2016.07.004zbMath1364.62203OpenAlexW2518437604MaRDI QIDQ334304

Yan Ping Liu, Zhigen Zhao, Sanat Kumar Sarkar

Publication date: 1 November 2016

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2016.07.004

zbMATH Keywords

false discovery rategrouped hypotheseslarge-scale multiple testing

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Paired and multiple comparisons; multiple testing (62J15)

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LAWS: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing, Heteroscedasticity-Adjusted Ranking and Thresholding for Large-Scale Multiple Testing, Where to find needles in a haystack?, Weighted False Discovery Rate Control in Large-Scale Multiple Testing, Weighted multiple testing procedure for grouped hypotheses with \(k\)-FWER control, Further results on controlling the false discovery rate under some complex grouping structure of hypotheses, Change-detection-assisted multiple testing for spatiotemporal data, Asymptotic theory of dependent Bayesian multiple testing procedures under possible model misspecification, Adapting to one- and two-way classified structures of hypotheses while controlling the false discovery rate, Uniformly consistently estimating the proportion of false null hypotheses via Lebesgue-Stieltjes integral equations, On the development of a local FDR-based approach to testing two-way classified hypotheses, Covariate-Assisted Ranking and Screening for Large-Scale Two-Sample Inference, Post-Selection Inference Following Aggregate Level Hypothesis Testing in Large-Scale Genomic Data, Local false discovery rate based methods for multiple testing of one-way classified hypotheses

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