Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes.

ABSTRACT: This paper develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered. A two-step weighted bias-correction method is proposed for constructing confidence intervals and simultaneous hypothesis tests for individual components of the regression vector. Minimax lower bound for the expected length is established and the proposed confidence intervals are shown to be rate-optimal up to a logarithmic factor. The numerical performance of the proposed procedure is demonstrated through simulation studies and an analysis of a single cell RNA-seq data set, which yields interesting biological insights that integrate well into the current literature on the c

SUBMITTER: Cai TT 

PROVIDER: S-EPMC10292730 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature