Variable selection in convex quantile regression: \(\mathcal{L}_1\)-norm or \(\mathcal{L}_0\)-norm regularization?
From MaRDI portal
Publication:2083962
DOI10.1016/j.ejor.2022.05.041OpenAlexW3178970341MaRDI QIDQ2083962
Publication date: 17 October 2022
Published in: European Journal of Operational Research (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2107.03119
Related Items (3)
Non-crossing convex quantile regression ⋮ Generalized quantile and expectile properties for shape constrained nonparametric estimation ⋮ Robust regression under the general framework of bounded loss functions
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Breaking the curse of dimensionality in nonparametric testing
- A unified approach to model selection and sparse recovery using regularized least squares
- The Adaptive Lasso and Its Oracle Properties
- Best subset selection via a modern optimization lens
- Faithful variable screening for high-dimensional convex regression
- Stepwise selection of variables in data envelopment analysis: procedures and managerial perspectives
- One-step sparse estimates in nonconcave penalized likelihood models
- Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction
- Quantile regression for robust bank efficiency score estimation
- Optimal rates of convergence for nonparametric estimators
- Segmented concave least squares: a nonparametric piecewise linear regression
- Dimension reduction in nonparametric models of production
- Shadow prices and marginal abatement costs: convex quantile regression approach
- Sparse high-dimensional regression: exact scalable algorithms and phase transitions
- Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons
- Nonparametric quantile frontier estimation under shape restriction
- The curse of dimensionality of decision-making units: a simple approach to increase the discriminatory power of data envelopment analysis
- Variable selection in data envelopment analysis via Akaike's information criteria
- Consistency of cross validation for comparing regression procedures
- A more efficient algorithm for convex nonparametric least squares
- Convex non-parametric least squares, causal structures and productivity
- An Algorithm for Data Envelopment Analysis
- Data Envelopment Analysis as Nonparametric Least-Squares Regression
- On Degrees of Freedom of Projection Estimators With Applications to Multivariate Nonparametric Regression
- Representation theorem for convex nonparametric least squares
- A Statistical Test for Nested Radial Dea Models
- Regression Quantiles
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Sparse Approximate Solutions to Linear Systems
- Sparse Convex Regression
- Binary classification with covariate selection through ℓ0-penalised empirical risk minimisation
- A Computational Framework for Multivariate Convex Regression and Its Variants
- Risk consistency of cross-validation with Lasso-type procedures
- JuMP: A Modeling Language for Mathematical Optimization
- Pitfalls and protocols in DEA
This page was built for publication: Variable selection in convex quantile regression: \(\mathcal{L}_1\)-norm or \(\mathcal{L}_0\)-norm regularization?
