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Simultaneous inference for generalized linear models with unmeasured confounders
April 23, 2024, 4:44 a.m. | Jin-Hong Du, Larry Wasserman, Kathryn Roeder
cs.LG updates on arXiv.org arxiv.org
Abstract: Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially biased. This paper investigates the large-scale hypothesis testing problem for multivariate generalized linear models in the presence of confounding effects. Under arbitrary confounding mechanisms, we propose a unified statistical estimation and inference framework that harnesses orthogonal structures and integrates linear projections into three key stages. …
abstract arxiv cs.lg generalized genes genomic however hypothesis identify inference linear multivariate paper q-bio.gn scale standard statistical stat.me stat.ml studies testing tests type
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