TMTianxing Mei
Tianxing Mei
*This is an algorithmically generated author record based on publicly available information.
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Tianxing Mei has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Chen Wang
Department of Statistics and Actuarial Science The University of Hong Kong
1 shared publication - Jianfeng Yao
School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China
1 shared publication
Latest Publications
- Open Access
Many-sample tests for the dimensionality hypothesis for large covariance matrices among groups
In this paper, we consider procedures for testing hypotheses on the dimension of the linear span generated by a growing number of $p\times p$ covariance matrices from independent $q$ populations. Under a proper limiting scheme where all the parameters, $q$, $p$, and the sample sizes from the $q$ populations, are allowed to increase to infinity, we derive the asymptotic normality of the proposed test statistics. The proposed test procedures show satisfactory performance in finite samples under both the null and the alternative. We also apply the proposed many-sample dimensionality test to investigate a matrix-valued gene dataset from the Mouse Aging Project and gain some new knowledge about its covariance structures.Open AccessMany-sample tests for the dimensionality hypothesis for large covariance matrices among groups
In this paper, we consider procedures for testing hypotheses on the dimension of the linear span generated by a growing number of $p\times p$ covariance matrices from independent $q$ populations. Under a proper limiting scheme where all the parameters, $q$, $p$, and the sample sizes from the $q$ populations, are allowed to increase to infinity, we derive the asymptotic normality of the proposed test statistics. The proposed test procedures show satisfactory performance in finite samples under both the null and the alternative. We also apply the proposed many-sample dimensionality test to investigate a matrix-valued gene dataset from the Mouse Aging Project and gain some new knowledge about its covariance structures.