Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”
From MaRDI portal
Publication:5146024
DOI10.1080/01621459.2020.1843865zbMath1452.62526OpenAlexW3114142462MaRDI QIDQ5146024
Lan Wang, Jelena Bradic, Bo Peng, Yunan N. Wu, Run-Ze Li
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/10.1080/01621459.2020.1843865
Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)
Related Items
TFRE ⋮ High-dimensional composite quantile regression: optimal statistical guarantees and fast algorithms ⋮ Scale calibration for high-dimensional robust regression
Cites Work
- On asymptotically optimal confidence regions and tests for high-dimensional models
- A general theory of hypothesis tests and confidence regions for sparse high dimensional models
- High-dimensional robust precision matrix estimation: cellwise corruption under \(\epsilon \)-contamination
- Statistical consistency and asymptotic normality for high-dimensional robust \(M\)-estimators
- Robust sparse covariance estimation by thresholding Tyler's M-estimator
- Scaled sparse linear regression
- Robust estimation of high-dimensional covariance and precision matrices
This page was built for publication: Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”
