Covariance structure regularization via entropy loss function

DOI10.1016/j.csda.2013.10.004zbMath1506.62109OpenAlexW2134910934WikidataQ56998653 ScholiaQ56998653MaRDI QIDQ1623420

Nicholas J. Higham, Lijing Lin, Jian-Xin Pan

Publication date: 23 November 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.2013.10.004

zbMATH Keywords

regularizationKullback-Leibler divergencecovariance structurecovariance estimationentropy loss function

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Estimation in multivariate analysis (62H12)

Related Items (11)

Regularization for high-dimensional covariance matrix ⋮ Correlation structure regularization via entropy loss function for high-dimension and low-sample-size data ⋮ The comparison of the estimators of banded toeplitz covariance structure under the high-dimensional multivariate model ⋮ Stability of principal components under normal and non-normal parent populations and different covariance structures scenarios ⋮ Block matrix approximation via entropy loss function. ⋮ Approximation with a Kronecker product structure with one component as compound symmetry or autoregression via entropy loss function ⋮ Covariance structure regularization via Frobenius-norm discrepancy ⋮ Estimation and optimal structure selection of high-dimensional Toeplitz covariance matrix ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Approximate normality in testing an exchangeable covariance structure under large- and high-dimensional settings

Uses Software

• mctoolbox

Cites Work

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