Two-way MANOVA with unequal cell sizes and unequal cell covariance matrices in high-dimensional settings

Hiroki Watanabe, Masashi Hyodo and Shigekazu Nakagawa

Journal of Multivariate Analysis, 2020, vol. 179, issue C

Abstract: In this paper, we discuss a two-way multivariate analysis of variance in high-dimensional settings. With a high-dimensional setting, we propose new approximate tests that work well under the following conditions: 1. The error vectors do not necessarily follow a multivariate normal distribution, 2. The cell sizes are unequal, 3. The cell covariance matrices are unequal, and 4. The dimension p is much larger than the total cell size n. The accuracy of the proposed tests with finite samples is shown through simulations for a variety of high-dimensional scenarios.

Keywords: MANOVA; Testing hypotheses; High-dimensional data analysis (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.jmva.2020.104625

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