Efficient Signal Inclusion With Genomic Applications

DOI10.1080/01621459.2018.1518236zbMath1428.62249arXiv1805.10570OpenAlexW2889253308WikidataQ92590698 ScholiaQ92590698MaRDI QIDQ5208082

X. Jessie Jeng, Teng Zhang, Jung-Ying Tzeng

Publication date: 15 January 2020

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

Full work available at URL: https://arxiv.org/abs/1805.10570

zbMATH Keywords

dimension reductionvariable screeningultrahigh dimensionfalse-negative controlfalse-positive control

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15) Paired and multiple comparisons; multiple testing (62J15)

Related Items

Estimating the proportion of signal variables under arbitrary covariance dependence, A survey of nonparametric mixing density estimation via the predictive recursion algorithm, Model selection with mixed variables on the Lasso path

Uses Software

• GCTA

Cites Work

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• Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses
• A stochastic process approach to false discovery control.
• Estimation and confidence sets for sparse normal mixtures
• False discovery and false nondiscovery rates in single-step multiple testing procedures
• Generalizations of the familywise error rate
• Detection of gene-gene interactions using multistage sparse and low-rank regression
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
• A Direct Approach to False Discovery Rates
• Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
• Estimating the Null and the Proportion of Nonnull Effects in Large-Scale Multiple Comparisons
• Correlation and Large-Scale Simultaneous Significance Testing
• Optimal Screening and Discovery of Sparse Signals with Applications to Multistage High Throughput Studies