Dimension Reduction for Detecting a Difference in Two High-Dimensional Mean Vectors

• 作者: Whitney V. Worley Dean M. Young Phil D. Young
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Homoscedastic Covariance Matrices Test Power Monte Carlo Simulation Moore-Penrose Inverse Singular Value Decomposition
• 年，卷（期）: 2021,（1）
• 页码范围: 243-257
• 页数: 15页
• 分类号: R57

摘要

We consider the efficacy of a proposed linear-dimension-reduction method to potentially increase the powers of five hypothesis tests for the difference of two high-dimensional multivariate-normal population-mean vectors with the assumption of homoscedastic covariance matrices. We use Monte Carlo simulations to contrast the empirical powers of the five high-dimensional tests by using both the original data and dimension-reduced data. From the Monte Carlo simulations, we conclude that a test by Thulin [1], when performed with post-dimension-reduced data, yielded the best omnibus power for detecting a difference between two high-dimensional population-mean vectors. We also illustrate the utility of our dimension-reduction method real data consisting of genetic sequences of two groups of patients with Crohn’s disease and ulcerative colitis.