A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices
arXiv:1603.01003 · doi:10.1007/s11425-016-0131-0
Abstract
In this paper, we will introduce the so called naive tests and give a brief review on the newly development. Naive testing methods are easy to understand and performs robust especially when the dimension is large. In this paper, we mainly focus on reviewing some naive testing methods for the mean vectors and covariance matrices of high dimensional populations and believe this naive test idea can be wildly used in many other testing problems.
References in corpus (6)
- A two-sample test for high-dimensional data with applications to gene-set testing
- Two sample tests for high-dimensional covariance matrices
- Test for bandedness of high-dimensional covariance matrices and bandwidth estimation
- A high-dimensional two-sample test for the mean using random subspaces
- Tests for covariance matrix with fixed or divergent dimension
- Testing the Mean Matrix in High-Dimensional Transposable Data