A Random Projection Approach to Hypothesis Tests in High-Dimensional Single-Index Models

Changyu Liu, Xingqiu Zhao, Jian Huang

Abstract:

In this article, we consider the problem of hypothesis testing in high-dimensional single-index models.
First, we study the feasibility of applying the classical F-test to a single-index model when the dimension
of covariate vector and sample size are of the same order, and derive its asymptotic null distribution and
asymptotic local power function. For the ultrahigh-dimensional single-index model, we construct F-statistics
based on lower-dimensional random projections of the data, and establish the asymptotic null distribution
and the asymptotic local power function of the proposed test statistics for the hypothesis testing of global
and partial parameters. The new proposed test possesses the advantages of having a simple structure as well
as being easy to compute. We compare the proposed test with other high-dimensional tests and provide
sufficient conditions under which the proposed tests are more efficient. We conduct simulation studies to
evaluate the finite-sample performances of the proposed tests and demonstrate that it has higher power
than some existing methods in the models we consider. The application of real high-dimensional gene
expression data is also provided to illustrate the effectiveness of the method. Supplementary materials for
this article are available online.