Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions.

ABSTRACT: Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates while controlling the false discovery rate and false discovery proportion. Theoretical justifications are provided to guarantee the validity of the proposed tests and optimality results are established under sparsity assumptions on the regression coefficients. The proposed testing procedures are easy to implement. Numerical properties of the procedures are investigated through simulation and data

SUBMITTER: Xia Y 

PROVIDER: S-EPMC5788049 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature