Chia-Hsuan Tsai | Searcharxiv

arXiv2024

On the orthogonally equivariant estimators of a covariance matrix

In this note, when the dimension $p$ is large we look into the insight of the Mar$\check{c}$enko-Pastur equation to get an explicit equality relationship, and use the obtained equality to establish a new kind of orthogonally equivariant estimator of the population covariance matrix. Under some regularity conditions, the proposed novel estimators of the population eigenvalues are shown to be consistent for the eigenvalues of population covariance matrix. It is also shown that the proposed estimator is the best orthogonally equivariant estimator of population covariance matrix under the normalized Stein loss function.

math.ST↗

arXiv2024

The decomposite $T^{2}$-test when the dimension is large

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 $T^{2}$-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 $T^{2}$-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.

math.ST↗