Computable error bounds for high-dimensional Edgeworth expansions in sphericity testing under two-step monotone incomplete data

TL;DR

This paper derives computable Edgeworth expansions for the null distribution of likelihood ratio tests in high-dimensional sphericity testing with incomplete data, improving approximation accuracy over existing methods.

Contribution

It introduces new Edgeworth expansions with explicit error bounds for high-dimensional sphericity tests under two-step monotone incomplete data, enhancing null distribution approximations.

Findings

Edgeworth expansions outperform existing asymptotic expansions in accuracy.

Numerical simulations confirm the effectiveness of the computable error bounds.

Proposed methods improve the reliability of high-dimensional sphericity testing.

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 are inaccurate in high-dimensional settings. Therefore, we derive Edgeworth expansions for the null distribution of the LRT statistic in such settings and obtain computable error bounds. Furthermore, we demonstrate that our proposed Edgeworth expansions provide better approximation accuracy than the existing asymptotic expansions. We also conduct numerical experiments using Monte Carlo simulations to evaluate the maximum absolute error (MAE) between the distribution function of the standardized test statistic and Edgeworth expansions for the null distribution of the LRT statistic, as well as to assess the performance of the computable error bounds.