A simple forward selection procedure based on false discovery rate control

DOI10.1214/08-AOAS194zbMath1160.62068arXiv0905.2819WikidataQ30053869 ScholiaQ30053869MaRDI QIDQ1018611

Yoav Benjamini, Yulia Gavrilov

Publication date: 20 May 2009

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

Full work available at URL: https://arxiv.org/abs/0905.2819

zbMATH Keywords

linear regressionmultiple testingrandom oracle

Mathematics Subject Classification ID

Statistical ranking and selection procedures (62F07) Paired and multiple comparisons; multiple testing (62J15)

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Uses Software

• EBayesThresh

Cites Work

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• A simple forward selection procedure based on false discovery rate control
• On the false discovery rate and an asymptotically optimal rejection curve
• An adaptive step-down procedure with proven FDR control under independence
• Some results on false discovery rate in stepwise multiple testing procedures.
• Least angle regression. (With discussion)
• Simultaneous analysis of Lasso and Dantzig selector
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Adapting to unknown sparsity by controlling the false discovery rate
• Empirical Bayes selection of wavelet thresholds
• Calibration and empirical Bayes variable selection
• Adaptive linear step-up procedures that control the false discovery rate
• The Covariance Inflation Criterion for Adaptive Model Selection
• Ideal spatial adaptation by wavelet shrinkage
• Adaptive Model Selection
• Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
• A Direct Approach to False Discovery Rates
• Operating Characteristics and Extensions of the False Discovery Rate Procedure
• Controlling Variable Selection by the Addition of Pseudovariables
• Robust Estimates of Location: Survey and Advances
• Some Comments on C P
• Variable Selection in Data Mining
• Gaussian model selection