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Linear Hypothesis Testing in Linear Models With High-Dimensional Responses.

ABSTRACT: In this paper, we propose a new projection test for linear hypotheses on regression coefficient matrices in linear models with high dimensional responses. We systematically study the theoretical properties of the proposed test. We first derive the optimal projection matrix for any given projection dimension to achieve the best power and provide an upper bound for the optimal dimension of projection matrix. We further provide insights into how to construct the optimal projection matrix. One- and two-sample mean problems can be formulated as special cases of linear hypotheses studied in this paper. We both theoretically and empirically demonstrate that the proposed test can outperform the existing ones for one- and two-sample mean problems. We conduct Monte Carlo simulation to examine the finite sample performance and illustrate the proposed test by a real data example.

SUBMITTER: Li C

PROVIDER: S-EPMC9996668 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Linear Hypothesis Testing in Linear Models With High-Dimensional Responses.

Li Changcheng C Li Runze R

Journal of the American Statistical Association 20210427 540

In this paper, we propose a new projection test for linear hypotheses on regression coefficient matrices in linear models with high dimensional responses. We systematically study the theoretical properties of the proposed test. We first derive the optimal projection matrix for any given projection dimension to achieve the best power and provide an upper bound for the optimal dimension of projection matrix. We further provide insights into how to construct the optimal projection matrix. One- and ...[more]

PMID: 36908313

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