.png){kind=icon}
返回
A Bayesian discovery procedure
.png){kind=icon}
OA
AI
DOI：10.1111/j.1467-9868.2009.00714.x.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We discuss a Bayesian discovery procedure for multiple-comparison problems. We show that, under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule that is based on a threshold of the posterior probability of the alternative. Under a semiparametric model for the data, we show that the Bayes rule can be approximated by the optimal discovery procedure, which was recently introduced by Storey. Improving the approximation leads us to a Bayesian discovery procedure, which exploits the multiple shrinkage in clusters that are implied by the assumed non-parametric model. We compare the Bayesian discovery procedure and the optimal discovery procedure estimates in a simple simulation study and in an assessment of differential gene expression based on microarray data from tumour samples. We extend the setting of the optimal discovery procedure by discussing modifications of the loss function that lead to different single-thresholding statistics. Finally, we provide an application of the previous arguments to dependent (spatial) data.
Keyword:
Bayes optimal rule
False discovery rate
Loss function
Multiple comparison
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
机构
引用论文
分享收藏Significance analysis of microarrays applied to the ionizing radiation response微阵列应用于电离辐射响应的意义分析OAAI
OAAI分享收藏
Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset.png)
GENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAI分享收藏分享收藏
查看更多内容