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Activity Number: 60 - Nonparametrics in High-Dimensional Data
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Nonparametric Statistics
Abstract #313665
Title: A Hybrid Omnibus Test in Generalized Semiparametric Single Index Models with High-Dimensional Covariate Sets
Author(s): Inyoung Kim* and Yangyi Xu and Raymond Carroll
Companies: Virginia Tech and Virginia Tech and Texas A&M
Keywords: Hybrid test
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 paper, we propose a hybrid omnibus test for high-dimensional 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 frequentest-Bayesian hybrid score-type test for a generalized partially linear single index model, which has a link function being a functional of a set of variables through a generalized partially linear single index. We propose an efficient score based on estimating equations, define local tests, and then construct our hybrid omnibus test using local tests. Our simulation results suggest that our approach outperforms the others, in terms of Type I error, power, and computational cost in both the low-and high-dimensional cases. The advantage of our approach is demonstrated by applying it to genetic pathway data for type II diabetes mellitus.


Authors who are presenting talks have a * after their name.

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