Discussion of “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”

DOI10.1080/01621459.2020.1837139zbMath1452.62520OpenAlexW3118091055MaRDI QIDQ5146022

Xiudi Li, Ali Shojaie

Publication date: 22 January 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2020.1837139

zbMATH Keywords

robustnessefficiencylinear regressionhigh dimensiontuning parameterheavy-tailed error

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Uses Software

• glasso

Cites Work

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• Estimation of high-dimensional graphical models using regularized score matching
• Sparse inverse covariance estimation with the graphical lasso
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• Regularized rank-based estimation of high-dimensional nonparanormal graphical models
• High-dimensional semiparametric Gaussian copula graphical models
• High-dimensional graphs and variable selection with the Lasso
• A permutation approach for selecting the penalty parameter in penalized model selection
• Scaled sparse linear regression
• Model selection and estimation in the Gaussian graphical model
• Selection and estimation for mixed graphical models
• High Dimensional Semiparametric Latent Graphical Model for Mixed Data
• Graph estimation with joint additive models

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