Global Testing of Gaussian Means by Combining Marginal and Conditional Evidence
Abstract
We propose a test of the global null hypothesis of zero means in correlated multivariate Gaussian data by combining marginal and conditional evidence at each coordinate. The conditional tests are based on the distribution of each coordinate given the remaining coordinates. At each coordinate, the corresponding two-sided p-values are combined using the Simes method, followed by a Bonferroni correction across coordinates. Because conditioning can either strengthen or weaken evidence against the null, neither marginal nor conditional testing is uniformly preferable. The proposed procedure adapts to their relative strengths while retaining the simplicity of minimum-type global testing. We establish finite-sample control of the global Type-I error under both known covariance and covariance estimated from an independent sample. The latter setting requires a careful analysis of the dependence between studentized marginal and conditional statistics. We evaluate the power of the proposed test through simulations, compare its performance with existing global tests, and illustrate its practical utility using a TCGA carcinoma dataset.
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BibTeX
@misc{ghosh2026global,
title = {{Global Testing of Gaussian Means by Combining Marginal and Conditional Evidence}},
author = {Deepra Ghosh and Edgar Dobriban and Sanat K. Sarkar},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40899},
eprint = {2610.40899},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40899},
}
RIS
TY - UNPB
TI - Global Testing of Gaussian Means by Combining Marginal and Conditional Evidence
AU - Deepra Ghosh
AU - Edgar Dobriban
AU - Sanat K. Sarkar
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40899
UR - https://arxiv.org/abs/2610.40899
AB - We propose a test of the global null hypothesis of zero means in correlated multivariate Gaussian data by combining marginal and conditional evidence at each coordinate. The conditional tests are based on the distribution of each coordinate given the remaining coordinates. At each coordinate, the corresponding two-sided p-values are combined using the Simes method, followed by a Bonferroni correction across coordinates. Because conditioning can either strengthen or weaken evidence against the null, neither marginal nor conditional testing is uniformly preferable. The proposed procedure adapts to their relative strengths while retaining the simplicity of minimum-type global testing. We establish finite-sample control of the global Type-I error under both known covariance and covariance estimated from an independent sample. The latter setting requires a careful analysis of the dependence between studentized marginal and conditional statistics. We evaluate the power of the proposed test through simulations, compare its performance with existing global tests, and illustrate its practical utility using a TCGA carcinoma dataset.
ER -