Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models.

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 stu

SUBMITTER: Ma R 

PROVIDER: S-EPMC8375316 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature