Fixed support positive-definite modification of covariance matrix estimators via linear shrinkage

DOI10.1016/j.jmva.2018.12.002zbMath1417.62141arXiv1606.03814OpenAlexW2613251611WikidataQ128767288 ScholiaQ128767288MaRDI QIDQ2418516

Johan Lim, Young-Geun Choi, Anindya Roy, Junyong Park

Publication date: 27 May 2019

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1606.03814

zbMATH Keywords

positive definitenesscovariance matrixportfolio optimizationprecision matrixhigh-dimensional estimationlinear shrinkagelinear minimax classification problem

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

High-dimensional Markowitz portfolio optimization problem: empirical comparison of covariance matrix estimators ⋮ Positive-definite modification of a covariance matrix by minimizing the matrix \(\ell_{\infty}\) norm with applications to portfolio optimization ⋮ Monitoring multivariate data with high missing rate by pooling univariate statistics

Uses Software

• EIGIFP
• eigs
• glasso
• IRAM
• spcov

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