ArticleAnnals of statistics2019
TEST FOR HIGH DIMENSIONAL CORRELATION MATRICES.
Article in Annals of statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Use of genetic correlations to examine selection bias.Genetic epidemiology · 2025Article
- Use of random integration to test equality of high dimensional covariance matrices.Statistica Sinica · 2023Article
- Debiased personalized gene coexpression networks for population-scale scRNA-seq data.Genome research · 2023Article
- Dozer: Debiased personalized gene co-expression networks for population-scale scRNA-seq data.bioRxiv : the preprint server for biology · 2023Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
Testing correlation structures has attracted extensive attention in the literature due to both its importance in real applications and several major theoretical challenges. The aim of this paper is to develop a general framework of testing correlation structures for the one-, two-, and multiple sample testing problems under a high-dimensional setting when both the sample size and data dimension go to infinity. Our test statistics are designed to deal with both the dense and sparse alternatives. We systematically investigate the asymptotic null distribution, power function, and unbiasedness of each test statistic. Theoretically, we make great efforts to deal with the non-independency of all random matrices of the sample correlation matrices. We use simulation studies and real data analysis to illustrate the versatility and practicability of our test statistics.
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Registered trials
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