Document Type
Lecture
Publication Date
4-24-2017
Abstract
The classic likelihood ratio test for testing the equality of two covariance matrices break- downs due to the singularity of the sample covariance matrices when the data dimension p is larger than the sample size n. In this paper, we present a conceptually simple method using random projection to project the data onto the one-dimensional random subspace so that the conventional methods can be applied. Both one-sample and two-sample tests for high-dimensional covariance matrices are studied. Asymptotic results are established and numerical results are given to compare our method with state-of-the-art methods in the literature.
Relational Format
presentation
Recommended Citation
Wu, Tung-Lung, "Tests for High-Dimensional Covariance Matrices Using Random Matrix Projection" (2017). Probability & Statistics Seminar. 34.
https://egrove.olemiss.edu/math_statistics/34
