Edgeworth expansions in sphericity testing under high-dimensional two-step monotone incomplete data and their computable error bounds

Tetsuya Sato; Tomoyuki Nakagawa

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arXiv · 2603.29120

Edgeworth expansions in sphericity testing under high-dimensional two-step monotone incomplete data and their computable error bounds

Tetsuya Sato·Tomoyuki Nakagawa

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Abstract

In this paper, we consider the sphericity test for a one-sample problem under high-dimensional two-step monotone incomplete data. Existing asymptotic expansions for the null distributions of the likelihood ratio test (LRT) statistic and modified LRT statistic deteriorate in high-dimensional settings. Therefore, we derive an Edgeworth expansion for the null distribution of the LRT statistic in such settings and obtain its computable error bounds. Furthermore, we demonstrate that our proposed Edgeworth expansion provide better approximation accuracy than the existing asymptotic expansion. We also conduct numerical experiments using Monte Carlo simulations to evaluate the maximum absolute error between the distribution function of the standardized test statistic and the Edgeworth expansion for the null distribution of the LRT statistic, as well as to assess the performance of the computable error bounds. Finally, by deriving a Cornish-Fisher expansion for the null distribution of the LRT statistic in high-dimensional settings, we evaluate the empirical Type I error rates via Monte Carlo simulations.

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Tetsuya Sato, Tomoyuki Nakagawa. 2026-03-31. Edgeworth expansions in sphericity testing under high-dimensional two-step monotone incomplete data and their computable error bounds. https://arxiv.org/abs/2603.29120

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