scholarly journals What’s Driving False Discovery Rates?

2008 ◽  
Vol 7 (1) ◽  
pp. 45-46 ◽  
Author(s):  
David L. Tabb
Keyword(s):  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates

False discovery rates and copy number variation

Biometrika ◽  
2011 ◽  
Vol 98 (2) ◽  
pp. 251-271 ◽  
Author(s):  
Bradley Efron ◽  
Nancy R. Zhang
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
False Discovery Rates ◽  
False Discovery ◽  
Number Variation ◽  
Discovery Rates

scholarly journals Signal identification for rare and weak features: higher criticism or false discovery rates?

Biostatistics ◽  
2012 ◽  
Vol 14 (1) ◽  
pp. 129-143 ◽  
Author(s):  
Bernd Klaus ◽  
Korbinian Strimmer
Keyword(s):  
Signal Identification ◽  
False Discovery Rates ◽  
Higher Criticism ◽  
False Discovery ◽  
Discovery Rates

scholarly journals A Comparison of Two Classes of Methods for Estimating False Discovery Rates in Microarray Studies

Scientifica ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Emily Hansen ◽  
Kathleen F. Kerr
Keyword(s):  
Differentially Expressed Genes ◽  
Null Distribution ◽  
Differentially Expressed ◽  
Test Statistics ◽  
False Discovery Rates ◽  
Model Method ◽  
False Discovery ◽  
Microarray Studies ◽  
Discovery Rates

The goal of many microarray studies is to identify genes that are differentially expressed between two classes or populations. Many data analysts choose to estimate the false discovery rate (FDR) associated with the list of genes declared differentially expressed. Estimating an FDR largely reduces to estimatingπ1, the proportion of differentially expressed genes among all analyzed genes. Estimatingπ1is usually done throughP-values, but computingP-values can be viewed as a nuisance and potentially problematic step. We evaluated methods for estimatingπ1directly from test statistics, circumventing the need to computeP-values. We adapted existing methodology for estimatingπ1fromt- andz-statistics so thatπ1could be estimated from other statistics. We compared the quality of these estimates to estimates generated by two established methods for estimatingπ1fromP-values. Overall, methods varied widely in bias and variability. The least biased and least variable estimates ofπ1, the proportion of differentially expressed genes, were produced by applying the “convest” mixture model method toP-values computed from a pooled permutation null distribution. Estimates computed directly from test statistics rather thanP-values did not reliably perform well.

scholarly journals False discovery rates: a new deal

Biostatistics ◽  
2016 ◽  
pp. kxw041 ◽  
Author(s):  
Matthew Stephens
Keyword(s):  
New Deal ◽  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates

Large and ancient linguistic areas

2020 ◽  
pp. 78-100
Author(s):  
Balthasar Bickel
Keyword(s):  
Regression Models ◽  
Large Scale ◽  
Population History ◽  
False Discovery Rates ◽  
Ancient Population ◽  
False Discovery ◽  
Language Universals ◽  
Discovery Rates ◽  
Pacific Area

Large-scale areal patterns point to ancient population history and form a well-known confound for language universals. Despite their importance, demonstrating such patterns remains a challenge. This chapter argues that large-scale area hypotheses are better tested by modeling diachronic family biases than by controlling for genealogical relations in regression models. A case study of the Trans-Pacific area reveals that diachronic bias estimates do not depend much on the amount of phylogenetic information that is used when inferring them. After controlling for false discovery rates, about 39 variables in WALS and AUTOTYP show diachronic biases that differ significantly inside vs. outside the Trans-Pacific area. Nearly three times as many biases hold outside than inside the Trans-Pacific area, indicating that the Trans-Pacific area is not so much characterized by the spread of biases but rather by the retention of earlier diversity, in line with earlier suggestions in the literature.

scholarly journals Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

PROTEOMICS ◽  
2009 ◽  
Vol 9 (5) ◽  
pp. 1220-1229 ◽  
Author(s):  
Andrew R. Jones ◽  
Jennifer A. Siepen ◽  
Simon J. Hubbard ◽  
Norman W. Paton
Keyword(s):  
Search Engines ◽  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates

scholarly journals False discovery rates, power and related concepts

2020 ◽  
Author(s):  
Richard G. Brereton
Keyword(s):  
False Discovery Rates ◽  
False Discovery ◽  
Discovery Rates
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