LINEAR HYPOTHESIS TESTING FOR HIGH DIMENSIONAL GENERALIZED LINEAR MODELS.

ABSTRACT: This paper is concerned with testing linear hypotheses in high-dimensional generalized linear models. To deal with linear hypotheses, we first propose constrained partial regularization method and study its statistical properties. We further introduce an algorithm for solving regularization problems with folded-concave penalty functions and linear constraints. To test linear hypotheses, we propose a partial penalized likelihood ratio test, a partial penalized score test and a partial penalized Wald test. We show that the limiting null distributions of these three test statistics are χ² distribution with the same degrees of freedom, and under local alternatives, they asymptotically follow non-central χ² distributions with the same degrees of freedom and noncentral para

SUBMITTER: Shi C 

PROVIDER: S-EPMC6750760 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature