2024/03/03 by Chia-Hsuan Tsai, Tsai, Chia-Hsuan, Ming‐Tien Tsai +1
Computer Science · Materials Science · Mathematics · #FOS: Mathematics #Graphite, nuclear technology, radiation studies #Mathematical Approximation and Integration #Medical Image Segmentation Techniques #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2403.01516
openalex publication_date 2024/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we discuss tests for mean vector of high-dimensional data when the dimension p is a function of sample size n. One of the tests, called the decomposite T2-test, in the high-dimensional testing problem is constructed based on the estimation work of Ledoit and Wolf (2018), which is an optimal orthogonally equivariant estimator of the inverse of population covariance matrix under Stein loss function. The asymptotic distribution function of the test statistic is investigated under a sequence of local alternatives. The asymptotic relative efficiency is used to see whether a test is optimal and to perform the power comparisons of tests. An application of the decomposite T2-test is in testing significance for the effect of monthly unlimited transport policy on public transportation, in which the data are taken from Taipei Metro System.