A penalized approach to covariate selection through quantile regression coefficient models
From MaRDI portal
Publication:4971512
DOI10.1177/1471082X19825523OpenAlexW2921010140MaRDI QIDQ4971512
Paolo Frumento, Matteo Bottai, Gianluca Sottile, Marcello Chiodi
Publication date: 12 October 2020
Published in: Statistical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1177/1471082x19825523
tuning parameter selectionLasso penaltyinspiratory capacitypenalized integrated loss minimization (\text{PILM})penalized quantile regression coefficients modelling \((\text{QRCM}_p)\)
Related Items (3)
Parametric expectile regression and its application for premium calculation ⋮ Robust estimation and regression with parametric quantile functions ⋮ qrcmNP
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Exact post-selection inference, with application to the Lasso
- Statistics for high-dimensional data. Methods, theory and applications.
- Asymptotic properties of criteria for selection of variables in multiple regression
- Estimated sampling distributions: The bootstrap and competitors
- Global nonparametric estimation of conditional quantile functions and their derivatives
- Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
- Estimating the dimension of a model
- Least angle regression. (With discussion)
- Adaptive penalized quantile regression for high dimensional data
- Least squares after model selection in high-dimensional sparse models
- A significance test for the lasso
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Parametric modeling of quantile regression coefficient functions
- Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
- Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selection
- p-Values for High-Dimensional Regression
- Regression Quantiles
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Parametric modeling of quantile regression coefficient functions with censored and truncated data
- Inference on Treatment Effects after Selection among High-Dimensional Controls
- 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
- False Discovery Control in Large-Scale Spatial Multiple Testing
- Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
- A new look at the statistical model identification
This page was built for publication: A penalized approach to covariate selection through quantile regression coefficient models
