Joint testing and false discovery rate control in high-dimensional multivariate regression
نویسندگان
چکیده
منابع مشابه
Joint Testing and False Discovery Rate Control in High-Dimensional Multivariate Regression
Multivariate regression with high-dimensional covariates has many applications in genomic 15 and genetic research, in which some covariates are expected to be associated with multiple responses. This paper considers joint testing for regression coefficients over multiple responses and develops simultaneous testing methods with false discovery rate control. The test statistic is based on inverse...
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Jian Huang1,5,∗, Jin Liu, Shuangge Ma, Cun-Hui Zhang and Yong Zhou Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa, U.S.A. Center of Quantitative Medicine, Duke-NUS Medical School,Singapore Department of Biostatistics, Yale University, New Haven, Connecticut, U.S.A. Department of Statistics and Biostatistics, Rutgers University, Piscataway, New Jersey, U.S.A....
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Many recently developed nonparametric jump tests can be viewed as multiple hypothesis testing problems. For such multiple hypothesis tests, it is well known that controlling type I error often makes a large proportion of erroneous rejections, and such situation becomes even worse when the jump occurrence is a rare event. To obtain more reliable results, we aim to control the false discovery rat...
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Background and Objectives: In recent years, new technologies have led to produce a large amount of data and in the field of biology, microarray technology has also dramatically developed. Meanwhile, the Fisher test is used to compare the control group with two or more experimental groups and also to detect the differentially expressed genes. In this study, the false discovery rate was investiga...
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Abstract: Multivariate statistics are often available as well as necessary in hypothesis tests. We study how to use such statistics to control not only false discovery rate (FDR) but also positive FDR (pFDR) with good power. We show that FDR can be controlled through nested regions of multivariate p-values of test statistics. If the distributions of the test statistics are known, then the regio...
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ژورنال
عنوان ژورنال: Biometrika
سال: 2018
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/asx085