Joint testing and false discovery rate control in high-dimensional multivariate regression

• 成果类型: Article
• 署名作者: Xia, Yin; Cai, T. Tony; Li, Hongzhe
• 署名单位: Fudan University; University of Pennsylvania; University of Pennsylvania
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx085
• 发表日期: 2018
• 页码: 249269
• 关键词: ovarian-cancer; confidence-intervals; association analysis; genetic association; linear-regression; cells; progression; phenotypes; inference

摘要

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.