ArticleJournal of the American Statistical Association2021
Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Adaptive Debiased Lasso in High-Dimensional Generalized Linear Models with Streaming Data.Journal of the American Statistical Association · 2026Article
- A Novel Approach of High Dimensional Linear Hypothesis Testing Problem.Journal of the American Statistical Association · 2025Article
- Debiased lasso after sample splitting for estimation and inference in high-dimensional generalized linear models.The Canadian journal of statistics = Revue canadienne de statistique · 2025Article
- Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer.Journal of the American Statistical Association · 2024Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- Debiased lasso for generalized linear models with a diverging number of covariates.Biometrics · 2023Article
- Article
- Comments on "A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models".Journal of the American Statistical Association · 2023Article
- Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes.Journal of the American Statistical Association · 2023Article
- Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.Biostatistics (Oxford, England) · 2022Article
- Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score.Journal of machine learning research : JMLR · 2022Article
- Double/debiased machine learning for logistic partially linear model.The econometrics journal · 2021Article
- Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.Journal of machine learning research : JMLR · 2021Article
- Inference for the Case Probability in High-dimensional Logistic Regression.Journal of machine learning research : JMLR · 2021Article
- A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models.Journal of machine learning research : JMLR · 2020Article
- Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction.Journal of machine learning research : JMLRArticle
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Authors and funding
3 authors.
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Abstract
High-dimensional logistic regression is widely used in analyzing data with binary outcomes. In this paper, global testing and large-scale multiple testing for the regression coefficients are considered in both single- and two-regression settings. A test statistic for testing the global null hypothesis is constructed using a generalized low-dimensional projection for bias correction and its asymptotic null distribution is derived. A lower bound for the global testing is established, which shows that the proposed test is asymptotically minimax optimal over some sparsity range. For testing the individual coefficients simultaneously, multiple testing procedures are proposed and shown to control the false discovery rate (FDR) and falsely discovered variables (FDV) asymptotically. Simulation studies are carried out to examine the numerical performance of the proposed tests and their superiority over existing methods. The testing procedures are also illustrated by analyzing a data set of a metabolomics study that investigates the association between fecal metabolites and pediatric Crohn's disease and the effects of treatment on such associations.
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