A0419
Title: A shrinkage likelihood ratio test for high-dimensional subgroup analysis with a logistic-normal mixture model
Authors: Shota Takeishi - University of Tokyo (Japan) [presenting]
Abstract: The focus is on testing the existence of a subgroup with an enhanced treatment effect under the setting where high-dimensional covariates potentially characterize the subgroup membership. Using a logistic-normal mixture model, the method proposed a shrinkage likelihood ratio test built on a modified likelihood function that shrinks high-dimensional unidentified parameters towards zero when there exists no subgroup is proposed. This shrinkage helps handle the irregularity of the testing problem in the logistic-normal mixture model. It enables us to derive a tractable chi-squired-type asymptotic null distribution even under the high-dimensional regime. Simulation results will also be provided.
