Ontology highlight
ABSTRACT:
SUBMITTER: Wu C
PROVIDER: S-EPMC7425805 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Wu Chong C Xu Gongjun G Shen Xiaotong X Pan Wei W
Journal of machine learning research : JMLR 20200726
In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type I error rates under high-dimensional nuisance parameter situations. In this paper, we propos ...[more]