Paper Info
Title
How Accurate are the Extremely Small P-values Used in Genomic Research: An Evaluation of Numerical Libraries.
Abstract
In the fields of genomics and high dimensional biology (HDB), massive multiple testing prompts the use of extremely small significance levels. Because tail areas of statistical distributions are needed for hypothesis testing, the accuracy of these areas is important to confidently make scientific judgments. Previous work on accuracy was primarily focused on evaluating professionally written statistical software, like SAS, on the Statistical Reference Datasets (StRD) provided by National Institute of Standards and Technology (NIST) and on the accuracy of tail areas in statistical distributions. The goal of this paper is to provide guidance to investigators, who are developing their own custom scientific software built upon numerical libraries written by others. In specific, we evaluate the accuracy of small tail areas from cumulative distribution functions (CDF) of the Chi-square and t-distribution by comparing several open-source, free, or commercially licensed numerical libraries in Java, C, and R to widely accepted standards of comparison like ELV and DCDFLIB. In our evaluation, the C libraries and R functions are consistently accurate up to six significant digits. Amongst the evaluated Java libraries, Colt is most accurate. These languages and libraries are popular choices among programmers developing scientific software, so the results herein can be useful to programmers in choosing libraries for CDF accuracy.
Year
DOI
Venue
2009
10.1016/j.csda.2008.11.028
Computational Statistics & Data Analysis
Keywords
Field
DocType
tail area,small tail area,cdf accuracy,statistical distribution,scientific software,small p-values,java library,statistical software,numerical library,genomic research,scientific judgment,r function,cumulative distribution function,hypothesis test,bioinformatics,multiple testing,biomedical research
Computer science,p-value,Multiple comparisons problem,Probability distribution,Software,NIST,Cumulative distribution function,Statistics,Java,Statistical hypothesis testing
Journal
Volume
Issue
ISSN
53
7
0167-9473
Citations 
PageRank 
References 
2
0.40
3
Authors
3
Name
Order
Citations
PageRank
Sai Santosh Bangalore120.40
Jelai Wang2311.85
David B Allison31389.96