Testing independence in high-dimensional multivariate normal data
From MaRDI portal
Publication:5078556
DOI10.1080/03610926.2019.1702699OpenAlexW2995659788MaRDI QIDQ5078556
Publication date: 23 May 2022
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2019.1702699
high-dimensional datamultivariate normal distributionmartingale central limit theoremtesting independence
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Central limit theorems for classical likelihood ratio tests for high-dimensional normal distributions
- A new test of independence for high-dimensional data
- Likelihood ratio tests for covariance matrices of high-dimensional normal distributions
- Testing the structure of the covariance matrix with fewer observations than the dimension
- \(L_p\)-norm spherical distribution
- Uniform distributions on spheres in finite dimensional \(L_\alpha\) and their generalizations
- On Schott's and Mao's test statistics for independence of normal random vectors
- Null distribution of the sum of squared \(z\)-transforms in testing complete independence
- Dependent central limit theorems and invariance principles
- Likelihood Ratio Tests for High‐Dimensional Normal Distributions
- Testing for complete independence in high dimensions
- A Significance Test for the Separation of Two Highly Multivariate Small Samples
- A High Dimensional Two Sample Significance Test
This page was built for publication: Testing independence in high-dimensional multivariate normal data