摘要翻译:
现代科学技术提供了一类新的大规模同时推理问题,需要同时考虑数以千计的假设检验。微阵列是这类技术的缩影,但在蛋白质组学、光谱学、成像和社会科学调查中也出现了类似的情况。本文利用错误发现率方法对大规模问题进行规模和功率计算。一种简单的经验贝叶斯方法允许错误发现率(fdr)分析在最小频率或贝叶斯建模假设下进行。导出了估计错误发现率的闭式精度公式,并用于比较不同的方法:局部或尾区FDR的,理论的,置换的,或经验的零假设估计。两个微阵列数据集和模拟被用来评估方法,功率诊断显示为什么非零病例很容易不能出现在“重要”发现的列表中。
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英文标题:
《Size, power and false discovery rates》
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作者:
Bradley Efron
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最新提交年份:
2007
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分类信息:
一级分类:Mathematics 数学
二级分类:Statistics Theory 统计理论
分类描述:Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计:例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类:Statistics 统计学
二级分类:Statistics Theory 统计理论
分类描述:stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近,贝叶斯推论,决策理论,估计,基础,推论,检验。
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英文摘要:
Modern scientific technology has provided a new class of large-scale simultaneous inference problems, with thousands of hypothesis tests to consider at the same time. Microarrays epitomize this type of technology, but similar situations arise in proteomics, spectroscopy, imaging, and social science surveys. This paper uses false discovery rate methods to carry out both size and power calculations on large-scale problems. A simple empirical Bayes approach allows the false discovery rate (fdr) analysis to proceed with a minimum of frequentist or Bayesian modeling assumptions. Closed-form accuracy formulas are derived for estimated false discovery rates, and used to compare different methodologies: local or tail-area fdr's, theoretical, permutation, or empirical null hypothesis estimates. Two microarray data sets as well as simulations are used to evaluate the methodology, the power diagnostics showing why nonnull cases might easily fail to appear on a list of ``significant'' discoveries.
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PDF链接:
https://arxiv.org/pdf/710.2245