Partially linear additive quantile regression in ultra-high dimension

DOI10.1214/15-AOS1367zbMath1331.62264arXiv1601.06000MaRDI QIDQ5963523

Lan Wang, Ben Sherwood

Publication date: 22 February 2016

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1601.06000

zbMATH Keywords

quantile regressionvariable selectionhigh dimensional datanonconvex penaltypartial linear

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35)

Related Items (30)

Sequential profile Lasso for ultra-high-dimensional partially linear models ⋮ Bayesian reciprocal LASSO quantile regression ⋮ Variable selection for additive partial linear quantile regression with missing covariates ⋮ CONDITIONAL MARGINAL TEST FOR HIGH DIMENSIONAL QUANTILE REGRESSION ⋮ Estimation and variable selection for partially linear additive models with measurement errors ⋮ Shrinkage estimation for identification of linear components in composite quantile additive models ⋮ Data Integration in High Dimension With Multiple Quantiles ⋮ Quantile regression for additive coefficient models in high dimensions ⋮ Forward variable selection for ultra-high dimensional quantile regression models ⋮ Subgroup analysis for heterogeneous additive partially linear models and its application to car sales data ⋮ Quantile regression of ultra-high dimensional partially linear varying-coefficient model with missing observations ⋮ A Review of Envelope Models ⋮ Predictive quantile regression with mixed roots and increasing dimensions: the ALQR approach ⋮ Quantile forward regression for high-dimensional survival data ⋮ Semiparametric model averaging for ultrahigh-dimensional conditional quantile prediction ⋮ Penalized averaging of parametric and non-parametric quantile forecasts ⋮ Locally sparse quantile estimation for a partially functional interaction model ⋮ FUNCTIONAL ADDITIVE QUANTILE REGRESSION ⋮ Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models ⋮ Adaptive varying-coefficient linear quantile model: a profiled estimating equations approach ⋮ Penalized weighted composite quantile regression for partially linear varying coefficient models with missing covariates ⋮ Screening and selection for quantile regression using an alternative measure of variable importance ⋮ Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity ⋮ Partially linear additive quantile regression in ultra-high dimension ⋮ Profile statistical inference for partially linear additive models with a diverging number of parameters ⋮ Adaptively weighted group Lasso for semiparametric quantile regression models ⋮ Unnamed Item ⋮ A robust spline approach in partially linear additive models ⋮ A spatial panel quantile model with unobserved heterogeneity ⋮ Estimation for partially linear additive regression with spatial data

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• Variable selection in high-dimensional quantile varying coefficient models
• Semi-varying coefficient models with a diverging number of components
• Estimation and variable selection for generalized additive partial linear models
• Conditional growth charts. (With discussion and rejoinder)
• On M-processes and M-estimation
• One-step sparse estimates in nonconcave penalized likelihood models
• Profile-kernel likelihood inference with diverging number of parameters
• Variable selection in nonparametric additive models
• SCAD-penalized regression in high-dimensional partially linear models
• Regularized simultaneous model selection in multiple quantiles regression
• Quantile regression in partially linear varying coefficient models
• Additive regression and other nonparametric models
• Limiting behavior of \(M\)-estimators of regression coefficients in high dimensional linear models. I: Scale-dependent case. II: Scale-invariant case
• Convex analysis approach to d. c. programming: Theory, algorithms and applications
• On parameters of increasing dimensions
• Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
• Bivariate tensor-product \(B\)-splines in a partly linear model
• Consistent covariate selection and post model selection inference in semiparametric regression.
• Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• Simultaneous multiple non-crossing quantile regression estimation using kernel constraints
• Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models
• Can Tests for Jumps be Viewed as Tests for Clusters?
• Estimation in a semiparametric model for longitudinal data with unspecified dependence structure
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection via Bayesian Information Criterion for Quantile Regression Models
• Shrinkage Estimation of the Varying Coefficient Model
• Variable Selection for Partially Linear Models With Measurement Errors
• Model Selection and Estimation in Regression with Grouped Variables
• Estimation and variable selection for semiparametric additive partial linear models
• Partially linear additive quantile regression in ultra-high dimension
• A selective review of group selection in high-dimensional models

This page was built for publication: Partially linear additive quantile regression in ultra-high dimension