The beta-binomial distribution for estimating the number of false rejections in microarray gene expression studies

DOI10.1016/j.csda.2008.01.013zbMath1453.62117OpenAlexW1967423625MaRDI QIDQ961329

Cheng Cheng, Stanley Pounds, Daniel L. Hunt

Publication date: 30 March 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc2845402

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20) Paired and multiple comparisons; multiple testing (62J15)

Related Items (4)

Unnamed Item ⋮ The Bayes rule of the parameter in (0,1) under the power-log loss function with an application to the beta-binomial model ⋮ Optimization heuristics for determining internal rating grading scales ⋮ Editorial: Statistical genetics \& statistical genomics: where biology, epistemology, statistics, and computation collide

Uses Software

• aod
• seq.f95

Cites Work

• Unnamed Item
• Unnamed Item
• A mixture model approach for the analysis of microarray gene expression data.
• The control of the false discovery rate in multiple testing under dependency.
• Estimation of False Discovery Rates in Multiple Testing: Application to Gene Microarray Data
• Comparison of Methods for Estimating the Number of True Null Hypotheses in Multiplicity Testing
• Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
• A Direct Approach to False Discovery Rates
• Statistical significance for genomewide studies
• Statistical Significance Threshold Criteria For Analysis of Microarray Gene Expression Data

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