Weighted \(\ell_1\)-penalized corrected quantile regression for high dimensional measurement error models
From MaRDI portal
Publication:495344
DOI10.1016/j.jmva.2015.04.009zbMath1327.62406OpenAlexW1988892377MaRDI QIDQ495344
Publication date: 10 September 2015
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2015.04.009
Related Items
Variable selection in high-dimensional linear model with possibly asymmetric errors, Unnamed Item, Weighted l1‐Penalized Corrected Quantile Regression for High‐Dimensional Temporally Dependent Measurement Errors
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Measurement error in Lasso: impact and likelihood bias correction
- The Adaptive Lasso and Its Oracle Properties
- Statistics for high-dimensional data. Methods, theory and applications.
- Sparse recovery under matrix uncertainty
- High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
- Fast global convergence of gradient methods for high-dimensional statistical recovery
- Simultaneous analysis of Lasso and Dantzig selector
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Adaptive robust variable selection
- Corrected-loss estimation for quantile regression with covariate measurement errors
- Deconvolving kernel density estimators
- Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Model Selection via Bayesian Information Criterion for Quantile Regression Models
- Regularization Parameter Selections via Generalized Information Criterion
- Chi-square oracle inequalities
- Tuning parameter selectors for the smoothly clipped absolute deviation method
- Measurement Error in Nonlinear Models
- Error Distribution for Gene Expression Data
