Improved multivariate normal mean estimation with unknown covariance when \(p\) is greater than \(n\)

DOI10.1214/12-AOS1067zbMath1296.62048arXiv1302.6746MaRDI QIDQ741819

Martin T. Wells, Didier Chételat

Publication date: 15 September 2014

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1302.6746

zbMATH Keywords

Moore-Penrose inverseminimax estimationlocation parametercovariance estimationsingular Wishart distributionrisk functionJames-Stein estimationinvariant quadratic losslarge-\(p\)-small-\(n\) problems

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Point estimation (62F10) Minimax procedures in statistical decision theory (62C20)

Related Items (12)

Improved second order estimation in the singular multivariate normal model ⋮ Estimation of a high-dimensional covariance matrix with the Stein loss ⋮ Non-parametric shrinkage mean estimation for quadratic loss functions with unknown covariance matrices ⋮ Scale matrix estimation of an elliptically symmetric distribution in high and low dimensions ⋮ Estimation of the inverse scatter matrix of an elliptically symmetric distribution ⋮ Erratum to: ``Improved multivariate normal mean estimation with unknown covariance when \(p\) is greater than \(n\) ⋮ Optimal shrinkage estimator for high-dimensional mean vector ⋮ Estimation of the mean vector in a singular multivariate normal distribution ⋮ Estimation of the inverse scatter matrix for a scale mixture of Wishart matrices under Efron-Morris type losses ⋮ Prediction and calibration for multiple correlated variables ⋮ Recent advances in shrinkage-based high-dimensional inference ⋮ A unified approach to estimating a normal mean matrix in high and low dimensions

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