Testing homogeneity of several covariance matrices and multi-sample sphericity for high-dimensional data under non-normality
From MaRDI portal
Publication:4976251
DOI10.1080/03610926.2015.1073310zbMath1368.62146OpenAlexW2343649160MaRDI QIDQ4976251
No author found.
Publication date: 27 July 2017
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2015.1073310
Related Items (2)
A new method for multi-sample high-dimensional covariance matrices test based on permutation ⋮ Homogeneity test of several high-dimensional covariance matrices for stationary processes under non-normality
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A test for the equality of covariance matrices when the dimension is large relative to the sample sizes
- Two sample tests for high-dimensional covariance matrices
- Decomposability of high-dimensional diversity measures: quasi-\(U\)-statistics, martingales and nonstandard asymptotics
- Weak convergence of generalized U-statistics
- Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
- A \(U\)-statistic approach for a high-dimensional two-sample mean testing problem under non-normality and Behrens-Fisher setting
- On some test criteria for covariance matrix
- A note on testing the covariance matrix for large dimension
- Approximation Theorems of Mathematical Statistics
- Invariance principles for von Mises and U-statistics
- On U-statistics and v. mise? statistics for weakly dependent processes
- Tests for High-Dimensional Covariance Matrices
- Tests of Covariance Matrices for High Dimensional Multivariate Data Under Non Normality
- A Class of Statistics with Asymptotically Normal Distribution
- Multivariate Statistics
This page was built for publication: Testing homogeneity of several covariance matrices and multi-sample sphericity for high-dimensional data under non-normality
