Improved Estimation of the Noncentrality Parameter Distribution from a Large Number of t‐Statistics, with Applications to False Discovery Rate Estimation in Microarray Data Analysis

DOI10.1111/j.1541-0420.2012.01764.xzbMath1274.62857OpenAlexW2015005246WikidataQ34253479 ScholiaQ34253479MaRDI QIDQ4911943

Jack C. M. Dekkers, Dan Nettleton, Long Qu

Publication date: 20 March 2013

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2012.01764.x

zbMATH Keywords

density estimationmicroarrayfalse discovery ratesnoncentrality parameter

Mathematics Subject Classification ID

Density estimation (62G07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Testing in survival analysis and censored data (62N03)

Related Items (3)

Estimating the proportion of true null hypotheses with application in microarray data ⋮ Sample size reassessment for a two-stage design controlling the false discovery rate ⋮ General power and sample size calculations for high-dimensional genomic data

Uses Software

• twilight
• TWILIGHT
• COBS

Cites Work

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• Estimating the dimension of a model
• COBS: qualitatively constrained smoothing via linear programming
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• Estimation of False Discovery Rates in Multiple Testing: Application to Gene Microarray Data
• Parametric and Nonparametric FDR Estimation Revisited
• Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control
• A Semiparametric Approach to Density Estimation
• A Proposed Design and Analysis for Comparing Digital and Analog Mammography
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
• Model Selection and Multimodel Inference
• Operating Characteristics and Extensions of the False Discovery Rate Procedure
• Estimating the Null and the Proportion of Nonnull Effects in Large-Scale Multiple Comparisons
• Exploring the Information in p‐Values for the Analysis and Planning of Multiple‐Test Experiments
• Statistical significance for genomewide studies
• Estimating the Proportion of True Null Hypotheses, with application to DNA Microarray Data

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