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| Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data |
| Hyoseok Ko, Kipoong Kim, Hokeun Sun |
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Genomics Inform. 2016;14(4):187-195. Published online December 30, 2016 DOI: https://doi.org/10.5808/GI.2016.14.4.187 |
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Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data Genomics & Informatics. 2016;14(4):187  Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sample sizes BMC Bioinformatics. 2008;9(1): Variable selection for high-dimensional genomic data with censored outcomes using group lasso prior Computational Statistics & Data Analysis. 2017;112:1-13 Testing the General Linear Hypothesis Large Sample Covariance Matrices and High-Dimensional Data Analysis. 2015;105-123 Weighted multiple testing procedures for genomic studies BioData Mining. 2012;5(1): Testing Independence of Sets of Variates Large Sample Covariance Matrices and High-Dimensional Data Analysis. 2015;124-139 Multiple testing procedures for analyzing stratified comparative clinical trials using odds ratios Computational Statistics & Data Analysis. 2006;50(11):3324-3342 Testing Hypotheses of Equality of Covariance Matrices Large Sample Covariance Matrices and High-Dimensional Data Analysis. 2015;140-171 On power and sample size computation for multiple testing procedures Computational Statistics & Data Analysis. 2011;55(1):110-122 Preselection in Lasso-Type Analysis for Ultra-High Dimensional Genomic Exploration Abel Symposia. 2016;37-66 |
