A hybrid omnibus test for generalized semiparametric single-index models with high-dimensional covariate sets.

ABSTRACT: Numerous statistical methods have been developed for analyzing high-dimensional data. These methods often focus on variable selection approaches but are limited for the purpose of testing with high-dimensional data. They are often required to have explicit-likelihood functions. In this article, we propose a "hybrid omnibus test" for high-dicmensional data testing purpose with much weaker requirements. Our hybrid omnibus test is developed under a semiparametric framework where a likelihood function is no longer necessary. Our test is a version of a frequentist-Bayesian hybrid score-type test for a generalized partially linear single-index model, which has a link function being a function of a set of variables through a generalized partially linear single index. We propose an efficient score

SUBMITTER: Xu Y 

PROVIDER: S-EPMC6736718 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature