Document Type

Article

Publication Date

4-2019

Publication Title

Electronic Journal of Statistics

Volume

13

Issue

2

Pages

4916-4944

Publisher Name

The Institute of Mathematical Statistics and the Bernoulli Society

Abstract

This paper proposes inferential methods for high-dimensional repeated measures in factorial designs. High-dimensional refers to the situation where the dimension is growing with sample size such that either one could be larger than the other. The most important contribution relates to high-accuracy of the methods in the sense that p-values, for example, are accurate up to the second-order. Second-order accuracy in sample size as well as dimension is achieved by obtaining asymptotic expansion of the distribution of the test statistics, and estimation of the parameters of the approximate distribution with second-order consistency. The methods are presented in a unified and succinct manner that it covers general factorial designs as well as any comparisons among the cell means. Expression for asymptotic powers are derived under two reasonable local alternatives. A simulation study provides evidence for a gain in accuracy and power compared to limiting distribution approximations and other competing methods for high-dimensional repeated measures analysis. The application of the methods are illustrated with a real-data from Electroencephalogram (EEG) study of alcoholic and control subjects.

Comments

Author Posting © Kong and Harrar, 2019. This article is posted here by permission of Kong and Harrar for personal use, not for redistribution. The article was published in JOURNAL, Volume 13, Issue 2, April 2019, https://projecteuclid.org/euclid.ejs/1575946866

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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