Tests of Covariance Matrices for High Dimensional Multivariate Data Under Non Normality
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Publication:5265837
DOI10.1080/03610926.2013.770533zbMath1320.62127OpenAlexW2018905710MaRDI QIDQ5265837
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Publication date: 29 July 2015
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2013.770533
Related Items (2)
Hypothesis Testing for the Covariance Matrix in High-Dimensional Transposable Data with Kronecker Product Dependence Structure ⋮ Testing homogeneity of several covariance matrices and multi-sample sphericity for high-dimensional data under non-normality
Cites Work
- A new test for sphericity of the covariance matrix for high dimensional data
- Some tests for the covariance matrix with fewer observations than the dimension under non-normality
- Analysis of high-dimensional repeated measures designs: the one sample case
- 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
- Statistical Methods for the Analysis of Repeated Measurements
- Approximation Theorems of Mathematical Statistics
- Modern Multivariate Statistical Techniques
- Tests for High-Dimensional Covariance Matrices
- A Class of Statistics with Asymptotically Normal Distribution
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