当前位置: X-MOL 学术IEEE Trans. Signal Process. › 论文详情
Distributions and Power of Optimal Signal-Detection Statistics in Finite Case
IEEE Transactions on Signal Processing ( IF 5.230 ) Pub Date : 2020-01-16 , DOI: 10.1109/tsp.2020.2967179
Hong Zhang; Jiashun Jin; Zheyang Wu

For detecting weak and sparse signals by a set of $n$ input $p$ -values, the Higher Criticism (HC) type statistics, the Berk-Jones (B-J) type statistics, and the phi-divergence statistics have the equivalent asymptotic optimality as $n$ goes to infinity. However, they can have significantly different performance in practical data analysis, where $n$ is always finite and even very small. To address this problem in a broader context, this paper introduces a general family of goodness-of-fit statistics, called the gGOF, which unifies a broad signal-detection statistics including these optimal ones. Efficient and accurate analytical calculations for the distributions of the gGOF statistics are provided under arbitrary i.i.d. continuous models of the null and the alternative hypotheses. Based on that, a systematic power study reveals that in finite case, the number of signals is often more relevant than the signal proportion. The HC and the reverse HC have advantages for relatively sparser and denser signals, respectively, while the B-J is more robust. A general framework is given to apply the gGOF into data analysis based on the generalized linear models. An application to the SNP-set based genome-wide association study (GWAS) for Crohn's disease shows that these optimal statistics have a good potential for detecting novel disease genes with weak SNP effects. The calculations have been implemented into an R package SetTest and published on the CRAN.
更新日期:2020-02-18

 

全部期刊列表>>
向世界展示您的会议墙报和演示文稿
全球疫情及响应:BMC Medicine专题征稿
欢迎探索2019年最具下载量的化学论文
新版X-MOL期刊搜索和高级搜索功能介绍
化学材料学全球高引用
ACS材料视界
x-mol收录
自然科研论文编辑服务
南方科技大学
南方科技大学
西湖大学
中国科学院长春应化所于聪-4-8
复旦大学
课题组网站
X-MOL
深圳大学二维材料实验室张晗
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug