Testing the order of a population spectral distribution for high-dimensional data
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Publication:1659483
DOI10.1016/j.csda.2015.09.009zbMath1468.62163OpenAlexW1834247060MaRDI QIDQ1659483
Publication date: 15 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2015.09.009
Computational methods for problems pertaining to statistics (62-08) Hypothesis testing in multivariate analysis (62H15)
Cites Work
- A new test for sphericity of the covariance matrix for high dimensional data
- Minimax bounds for sparse PCA with noisy high-dimensional data
- Optimal detection of sparse principal components in high dimension
- Estimation of the population spectral distribution from a large dimensional sample covariance matrix
- Some tests for the covariance matrix with fewer observations than the dimension under non-normality
- On testing for an identity covariance matrix when the dimensionality equals or exceeds the sample size
- Central limit theorem for signal-to-interference ratio of reduced rank linear receiver
- Spectrum estimation for large dimensional covariance matrices using random matrix theory
- Statistical eigen-inference from large Wishart matrices
- On the distribution of the largest eigenvalue in principal components analysis
- On generalized expectation-based estimation of a population spectral distribution from high-dimensional data
- Sparse PCA: optimal rates and adaptive estimation
- A determinant characterization of moment sequences with finitely many mass points
- Tests for High-Dimensional Covariance Matrices
- ON ESTIMATION OF THE POPULATION SPECTRAL DISTRIBUTION FROM A HIGH‐DIMENSIONAL SAMPLE COVARIANCE MATRIX
- A local moment estimator of the spectrum of a large dimensional covariance matrix
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