ArticleBiometrika2018
Joint testing and false discovery rate control in high-dimensional multivariate regression.
Article in Biometrika, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Model-Free Statistical Inference on High-Dimensional Data.Journal of the American Statistical Association · 2025Article
- Heterogeneous associations between interleukin-6 receptor variants and phenotypes across ancestries and implications for therapy.Scientific reports · 2024Article
- Comments on "A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models".Journal of the American Statistical Association · 2023Article
- Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.Journal of machine learning research : JMLR · 2021Article
- Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models.Journal of the American Statistical Association · 2021Article
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Authors and funding
3 authors.
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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.
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