Adaptive test for large covariance matrices with missing observations
From MaRDI portal
Publication:5278118
zbMath1366.62089arXiv1602.04310MaRDI QIDQ5278118
Rania Zgheib, Cristina Butucea
Publication date: 13 July 2017
Full work available at URL: https://arxiv.org/abs/1602.04310
goodness-of-fit testsToeplitz matricescovariance matricesadaptive testminimax separation ratemissing observation
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Sharp minimax tests for large Toeplitz covariance matrices with repeated observations
- Sharp minimax tests for large covariance matrices and adaptation
- High-dimensional covariance matrix estimation with missing observations
- Likelihood ratio tests for covariance matrices of high-dimensional normal distributions
- Corrections to LRT on large-dimensional covariance matrix by RMT
- Asymptotically minimax hypothesis testing for nonparametric alternatives. III
- Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size
- Nonparametric goodness-of-fit testing under Gaussian models
- Spectral analysis of high-dimensional sample covariance matrices with missing observations
- Optimal hypothesis testing for high dimensional covariance matrices
- Tests for High-Dimensional Covariance Matrices
- Introduction to nonparametric estimation
This page was built for publication: Adaptive test for large covariance matrices with missing observations