Empirical likelihood method for complete independence test on high-dimensional data
From MaRDI portal
Publication:5086106
DOI10.1080/00949655.2022.2029860OpenAlexW4210766409MaRDI QIDQ5086106
Publication date: 1 July 2022
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2201.08492
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Empirical likelihood approach to goodness of fit testing
- 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
- Empirical likelihood ratio confidence regions
- Corrections to LRT on large-dimensional covariance matrix by RMT
- On Schott's and Mao's test statistics for independence of normal random vectors
- Limiting distributions of likelihood ratio test for independence of components for high-dimensional normal vectors
- Likelihood Ratio Tests for High‐Dimensional Normal Distributions
- Testing for complete independence in high dimensions
- Empirical likelihood ratio confidence intervals for a single functional
- Berry-Esseen Inequality for Unbounded Exchangeable Pairs
This page was built for publication: Empirical likelihood method for complete independence test on high-dimensional data