ArticleStatistica Sinica2018
Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions.
Article in Statistica Sinica, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- A Novel Approach of High Dimensional Linear Hypothesis Testing Problem.Journal of the American Statistical Association · 2025Article
- TESTING HIGH-DIMENSIONAL REGRESSION COEFFICIENTS IN LINEAR MODELS.Annals of statistics · 2024Article
- Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks.Sankhya. Series A. (2008) · 2022Article
- Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach.Journal of the American Statistical Association · 2022Article
- 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
- Joint testing and false discovery rate control in high-dimensional multivariate regression.Biometrika · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates while controlling the false discovery rate and false discovery proportion. Theoretical justifications are provided to guarantee the validity of the proposed tests and optimality results are established under sparsity assumptions on the regression coefficients. The proposed testing procedures are easy to implement. Numerical properties of the procedures are investigated through simulation and data analysis. The results show that the proposed tests maintain the desired error rates under the null and have good power under the alternative at moderate sample sizes. The procedures are applied to the Framingham Offspring study to investigate the interactions between smoking and cardiovascular related genetic mutations important for an inflammation marker.
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Registered trials
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