Variable selection of varying coefficient models in quantile regression
From MaRDI portal
Publication:1950855
DOI10.1214/12-EJS709zbMath1295.62072OpenAlexW2089705522MaRDI QIDQ1950855
Kwanghun Chung, Hohsuk Noh, Ingrid Van Keilegom
Publication date: 28 May 2013
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.ejs/1341842803
shrinkage estimatorsecond order cone programmingbasis approximationconsistency in variable selection
Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric robustness (62G35) Statistical ranking and selection procedures (62F07)
Related Items (23)
Smooth-threshold estimating equations for varying coefficient partially nonlinear models based on orthogonality-projection method ⋮ Sparse Learning and Structure Identification for Ultrahigh-Dimensional Image-on-Scalar Regression ⋮ Regularization and model selection for quantile varying coefficient model with categorical effect modifiers ⋮ Modified adaptive group lasso for high-dimensional varying coefficient models ⋮ Variable Selection in Semiparametric Quantile Modeling for Longitudinal Data ⋮ M-estimation and model identification based on double SCAD penalization ⋮ Rank-based estimation in varying coefficient partially functional linear regression models ⋮ Variable selection of the quantile varying coefficient regression models ⋮ Variable selection in robust semiparametric modeling for longitudinal data ⋮ Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression ⋮ Semiparametric model averaging for ultrahigh-dimensional conditional quantile prediction ⋮ P-splines quantile regression estimation in varying coefficient models ⋮ Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations ⋮ Variable selection for spatial semivarying coefficient models ⋮ Variable selection for varying coefficient models via kernel based regularized rank regression ⋮ Walsh-average based variable selection for varying coefficient models ⋮ Expectile and quantile regression—David and Goliath? ⋮ Marginal quantile regression for varying coefficient models with longitudinal data ⋮ Simultaneous structure estimation and variable selection in partial linear varying coefficient models for longitudinal data ⋮ Robust variable selection in modal varying-coefficient models with longitudinal ⋮ Model Selection via Bayesian Information Criterion for Quantile Regression Models ⋮ Penalized kernel quantile regression for varying coefficient models ⋮ Variable selection for generalized varying coefficient models with longitudinal data
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- The Adaptive Lasso and Its Oracle Properties
- One-step sparse estimates in nonconcave penalized likelihood models
- Quantile regression with varying coefficients
- Quantile regression in partially linear varying coefficient models
- Applications of second-order cone programming
- Estimating the dimension of a model
- A practical guide to splines
- Second-order cone programming
- Quantile regression in varying coefficient models.
- Optimal global rates of convergence for nonparametric regression
- New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
- A unified variable selection approach for varying coefficient models
- Surface estimation, variable selection, and the nonparametric oracle property
- Quantile smoothing splines
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Identification of Non-Linear Additive Autoregressive Models
- Shrinkage Estimation of the Varying Coefficient Model
- Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
- Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models
- Model Selection and Estimation in Regression with Grouped Variables
This page was built for publication: Variable selection of varying coefficient models in quantile regression