协方差
一般线性模型
数学
统计
多元统计
统计假设检验
线性回归
协方差分析
线性模型
正规化(语言学)
多重比较问题
计量经济学
计算机科学
人工智能
作者
Haoran Li,Alexander Aue,Debashis Paul,Jie Peng
标识
DOI:10.1080/01621459.2023.2278825
摘要
We consider the problem of testing linear hypotheses under a multivariate regression model with a high-dimensional response and spiked noise covariance. The proposed family of tests consists of test statistics based on a weighted sum of projections of the data onto the estimated latent factor directions, with the weights acting as the regularization parameters. We establish asymptotic normality of the test statistics under the null hypothesis. We also establish the power characteristics of the tests and propose a data-driven choice of the regularization parameters under a family of local alternatives. The performance of the proposed tests is evaluated through a simulation study. Finally, the proposed tests are applied to the Human Connectome Project data to test for the presence of associations between volumetric measurements of human brain and behavioral variables.
科研通智能强力驱动
Strongly Powered by AbleSci AI