Modified Pillai's trace statistics for two high-dimensional sample covariance matrices
arXiv:2001.00397
Abstract
The goal of this study was to test the equality of two covariance matrices by using modified Pillai's trace statistics under a high-dimensional framework, i.e., the dimension and sample sizes go to infinity proportionally. In this paper, we introduce two modified Pillai's trace statistics and obtain their asymptotic distributions under the null hypothesis. The benefits of the proposed statistics include the following: (1) the sample size can be smaller than the dimensions; (2) the limiting distributions of the proposed statistics are universal; and (3) we do not restrict the structure of the population covariance matrices. The theoretical results are established under mild and practical assumptions, and their properties are demonstrated numerically by simulations and a real data analysis.
arXiv comment31 pages-to appear in Journal of Statistical Planning and Inference
References in corpus (6)
- Two sample tests for high-dimensional covariance matrices
- Test for bandedness of high-dimensional covariance matrices and bandwidth estimation
- Testing linear hypotheses in high-dimensional regressions
- A latent factor model with a mixture of sparse and dense factors to model gene expression data with confounding effects
- Convergence of the empirical spectral distribution function of Beta matrices
- Tests for High-Dimensional Covariance Matrices Using Random Matrix Projection