Optimal tests shrinking both means and variances applicable to microarray data analysis

DOI10.2202/1544-6115.1587zbMath1304.92044OpenAlexW2049399112WikidataQ33708141 ScholiaQ33708141MaRDI QIDQ2254481

Peng Liu, J. T. Gene Hwang

Publication date: 5 February 2015

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1058&context=stat_las_preprints

zbMATH Keywords

Neyman-Pearson lemmafalse discovery rate (FDR)\(F_{SS}\) test\(F_S\) testempirical Bayes test

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15)

Related Items (7)

Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices ⋮ Empirical Bayes Confidence Intervals for Selected Parameters in High-Dimensional Data ⋮ A method for modifying multiple testing procedures ⋮ Laplace approximated EM microarray analysis: an empirical Bayes approach for comparative microarray experiments ⋮ Robust semiparametric optimal testing procedure for multiple normal means ⋮ Applying shrinkage variance estimators to the TOST test in high dimensional settings ⋮ An Optimal Test with Maximum Average Power While Controlling FDR with Application to RNA‐Seq Data

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