A Random Matrix-Theoretic Approach to Handling Singular Covariance Estimates

DOI10.1109/TIT.2011.2162175zbMath1365.62202arXiv1010.0601OpenAlexW2964012924MaRDI QIDQ5272340

Thomas L. Marzetta, Steven H. Simon, Gabriel H. Tucci

Publication date: 12 July 2017

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1010.0601

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Random matrices (probabilistic aspects) (60B20) Random matrices (algebraic aspects) (15B52)

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On principal components regression, random projections, and column subsampling ⋮ Assessing the estimation of nearly singular covariance matrices for modeling spatial variables ⋮ A note on improving quadratic inference functions using a linear shrinkage approach ⋮ Increasing the accuracy of solving discrete ill-posed problems by the random projection method ⋮ Sufficient ensemble size for random matrix theory-based handling of singular covariance matrices ⋮ Compressed covariance estimation with automated dimension learning ⋮ Change-Point Detection of the Mean Vector with Fewer Observations than the Dimension Using Instantaneous Normal Random Projections ⋮ High-dimensional correlation matrix estimation for general continuous data with Bagging technique ⋮ Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions

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