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Joint testing and false discovery rate control in high-dimensional multivariate regression
- Source :
- Biometrika. 105:249-269
- Publication Year :
- 2018
- Publisher :
- Oxford University Press (OUP), 2018.
-
Abstract
- Multivariate regression with high-dimensional covariates has many applications in genomic 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 regression and bias-corrected group lasso estimates of the regression coefficients and is shown to have an asymptotic chi-squared null distribution. A row-wise multiple testing procedure is developed to identify the covariates associated with the responses. The procedure is shown to control the false discovery proportion and false discovery rate at a prespecified level asymptotically. Simulations demonstrate the gain in power, relative to entrywise testing, in detecting the covariates associated with the responses. The test is applied to an ovarian cancer dataset to identify the microRNA regulators that regulate protein expression.
- Subjects :
- 0301 basic medicine
Statistics and Probability
False discovery rate
Multivariate statistics
Applied Mathematics
General Mathematics
Articles
01 natural sciences
Agricultural and Biological Sciences (miscellaneous)
Regression
010104 statistics & probability
03 medical and health sciences
030104 developmental biology
Linear regression
Statistics
Multiple comparisons problem
Covariate
Null distribution
Test statistic
Statistics::Methodology
0101 mathematics
Statistics, Probability and Uncertainty
General Agricultural and Biological Sciences
Mathematics
Subjects
Details
- ISSN :
- 14643510 and 00063444
- Volume :
- 105
- Database :
- OpenAIRE
- Journal :
- Biometrika
- Accession number :
- edsair.doi.dedup.....361d5081dcef2068440084f27fe8a575