Precision matrix estimation under data contamination with an application to minimum variance portfolio selection

DOI10.1080/03610918.2019.1668012OpenAlexW2984286160MaRDI QIDQ5082899

Vahe Avagyan, Xiaoling Mei

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1668012

zbMATH Keywords

high-dimensional datarobust estimationGaussian graphical modeldata contaminationminimum variance portfolio

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• glasso

Cites Work

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• High-dimensional graphs and variable selection with the Lasso
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• Model selection and estimation in the Gaussian graphical model
• First-Order Methods for Sparse Covariance Selection
• Alternatives to the Median Absolute Deviation
• Partial Correlation Estimation by Joint Sparse Regression Models
• Sparse precision matrix estimation via lasso penalized D-trace loss

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