Robust estimation of sparse precision matrix using adaptive weighted graphical lasso approach-Reference-Cited by-同舟云学术

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Robust estimation of sparse precision matrix using adaptive weighted graphical lasso approach

Published:2021-06-02 Issue: Volume: Page:1-24
ISSN:1048-5252
Container-title:Journal of Nonparametric Statistics
language:en
Short-container-title:Journal of Nonparametric Statistics

Author:

Tang Peng¹,Jiang Huijing²,Kim Heeyoung³,Deng Xinwei⁴^ORCID

Affiliation:

1. AI, Cruise LLC, San Francisco, CA, USA

2. Global Biometrics and Data Sciences, Bristol Myers Squibb, Berkeley Heights, NJ, USA

3. Department of Industrial & Systems Engineering, KAIST, Daejeon, South Korea

4. Department of Statistics, Virginia Tech, Blacksburg, VA, USA

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10485252.2021.1931688

Reference36 articles.

1. Inferring sparse Gaussian graphical models with latent structure

2. Banerjee, O., El Ghaoui, L., and Georges, N. (2006), ‘Convex Optimization Techniques for Fitting Sparse Gaussian Graphical Models’, in Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, pp. 89–96.

3. Robust and efficient estimation by minimising a density power divergence

4. Convex Optimization

5. A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Robust and Sparse Estimation of Graphical Models Based on Multivariate Winsorization;Robust and Multivariate Statistical Methods;2022-11-26

2. Improvement on LASSO-type estimator in nonparametric regression;Journal of Nonparametric Statistics;2022-06-18

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