Feature selection in omics prediction problems using cat scores and false nondiscovery rate control

DOI10.1214/09-AOAS277zbMath1189.62102arXiv0903.2003OpenAlexW3101467075WikidataQ56504893 ScholiaQ56504893MaRDI QIDQ977653

Miika Ahdesmäki, Korbinian Strimmer

Publication date: 23 June 2010

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0903.2003

zbMATH Keywords

correlationJames-Stein estimatorhigher criticismfeature selectionlinear discriminant analysisfalse discovery ratescorrelation-adjusted t-scorelarge psmall n

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items

An adaptive decorrelation procedure for signal detection ⋮ Linear hypothesis testing in high-dimensional one-way MANOVA: a new normal reference approach ⋮ Stability of feature selection in classification issues for high-dimensional correlated data ⋮ Variational discriminant analysis with variable selection ⋮ Optimal Whitening and Decorrelation ⋮ Variational nonparametric discriminant analysis ⋮ Higher criticism for large-scale inference, especially for rare and weak effects

Uses Software

• R
• sda
• rda
• fdrtool
• corpor
• scout
• CMA

Cites Work

• Unnamed Item
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• Classifier technology and the illusion of progress
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• Higher criticism thresholding: Optimal feature selection when useful features are rare and weak
• The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis
• The Distribution of Chi-Square
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• Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach
• Large-Scale Simultaneous Hypothesis Testing
• Comments on: Augmenting the bootstrap to analyze high dimensional genomic data