Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension

DOI10.1080/01621459.2012.656014zbMath1328.62468OpenAlexW2137620021WikidataQ41989506 ScholiaQ41989506MaRDI QIDQ4916453

Yichao Wu, Lan Wang, Run-Ze Li

Publication date: 22 April 2013

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3471246

zbMATH Keywords

sparsitySCADpenalized quantile regressionultra-high-dimensional data

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

Related Items

Semiparametric model averaging for ultrahigh-dimensional conditional quantile prediction, Weighted expectile regression with covariates missing at random, Model averaging for support vector classifier by cross-validation, Rate-optimal robust estimation of high-dimensional vector autoregressive models, Robust Model Averaging Method Based on LOF Algorithm, Weighted l₁‐Penalized Corrected Quantile Regression for High‐Dimensional Temporally Dependent Measurement Errors, Retire: robust expectile regression in high dimensions, Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic, Low-Rank Regression Models for Multiple Binary Responses and their Applications to Cancer Cell-Line Encyclopedia Data, Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic, Inference for High-Dimensional Censored Quantile Regression, Cross-Fitted Residual Regression for High-Dimensional Heteroscedasticity Pursuit, Generalized martingale difference divergence: detecting conditional mean independence with applications in variable screening, A semi-parametric approach to feature selection in high-dimensional linear regression models, Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators, A smoothing iterative method for quantile regression with nonconvex \(\ell_p\) penalty, Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems, Variable selection in quantile varying coefficient models with longitudinal data, Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity, Regularized projection score estimation of treatment effects in high-dimensional quantile regression, Sparse Composite Quantile Regression with Ultra-high Dimensional Heterogeneous Data, Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data, Regularization and model selection for quantile varying coefficient model with categorical effect modifiers, Distributed optimization and statistical learning for large-scale penalized expectile regression, Variable selection in censored quantile regression with high dimensional data, Expectile regression for analyzing heteroscedasticity in high dimension, Variable selection for additive partial linear quantile regression with missing covariates, Robust error density estimation in ultrahigh dimensional sparse linear model, CONDITIONAL MARGINAL TEST FOR HIGH DIMENSIONAL QUANTILE REGRESSION, A proximal dual semismooth Newton method for zero-norm penalized quantile regression estimator, Oracle Estimation of a Change Point in High-Dimensional Quantile Regression, Two-layer EM algorithm for ALD mixture regression models: a new solution to composite quantile regression, Regularized estimation for the least absolute relative error models with a diverging number of covariates, Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits, Robust and efficient direction identification for groupwise additive multiple-index models and its applications, Variable selection and parameter estimation via WLAD-SCAD with a diverging number of parameters, Adaptive elastic net-penalized quantile regression for variable selection, Estimation and variable selection for a class of quantile regression models with multiple index, Regularized partially functional quantile regression, Multiply robust subgroup identification for longitudinal data with dropouts via median regression, Handling multicollinearity in quantile regression through the use of principal component regression, A penalized approach to covariate selection through quantile regression coefficient models, An improved algorithm for high-dimensional continuous threshold expectile model with variance heterogeneity, Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening, Robust variable selection based on the random quantile LASSO, A unified penalized method for sparse additive quantile models: an RKHS approach, Variable selection for high-dimensional varying coefficient partially linear models via nonconcave penalty, Hypothesis testing for regional quantiles, RDS free CLT for spiked eigenvalues of high-dimensional covariance matrices, Variable selection in high-dimensional quantile varying coefficient models, Penalized expectile regression: an alternative to penalized quantile regression, Globally adaptive quantile regression with ultra-high dimensional data, High-dimensional robust regression with \(L_q\)-loss functions, 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, High-dimensional latent panel quantile regression with an application to asset pricing, Zero-norm regularized problems: equivalent surrogates, proximal MM method and statistical error bound, Quantile regression of ultra-high dimensional partially linear varying-coefficient model with missing observations, Jackknife model averaging for high‐dimensional quantile regression, Group structure detection for a high‐dimensional panel data model, ADMM for Penalized Quantile Regression in Big Data, Robust direction identification and variable selection in high dimensional general single-index models, Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses, Variable selection in additive quantile regression using nonconcave penalty, Predictive quantile regression with mixed roots and increasing dimensions: the ALQR approach, Sequential change point detection for high‐dimensional data using nonconvex penalized quantile regression, Sparse and robust estimation with ridge minimax concave penalty, Quantile forward regression for high-dimensional survival data, Variable screening for ultrahigh dimensional censored quantile regression, Empirical likelihood in single-index quantile regression with high dimensional and missing observations, Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis, Sparse quantile regression, Nonparametric inference on smoothed quantile regression process, Smoothed quantile regression with large-scale inference, Adaptive robust variable selection, A systematic review on model selection in high-dimensional regression, Adaptive Lasso estimators for ultrahigh dimensional generalized linear models, Optimal computational and statistical rates of convergence for sparse nonconvex learning problems, A Tuning-free Robust and Efficient Approach to High-dimensional Regression, Weighted \(\ell_1\)-penalized corrected quantile regression for high dimensional measurement error models, Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations, FUNCTIONAL ADDITIVE QUANTILE REGRESSION, Conditional mean and quantile dependence testing in high dimension, High dimensional censored quantile regression, Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space, Iterative reweighted methods for \(\ell _1-\ell _p\) minimization, Adaptive varying-coefficient linear quantile model: a profiled estimating equations approach, Robust \(U\)-type test for high dimensional regression coefficients using refitted cross-validation variance estimation, A novel convex clustering method for high-dimensional data using semiproximal ADMM, The Lasso for High Dimensional Regression with a Possible Change Point, Elastic net penalized quantile regression model, Group identification and variable selection in quantile regression, 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, Quantile regression under memory constraint, Strong oracle optimality of folded concave penalized estimation, Robust network-based analysis of the associations between (epi)genetic measurements, Asymptotic properties of concave \(L_1\)-norm group penalties, Quantile regression for functional partially linear model in ultra-high dimensions, Simultaneous estimation of linear conditional quantiles with penalized splines, Adaptive sparse group LASSO in quantile regression, Model Selection via Bayesian Information Criterion for Quantile Regression Models, Scale calibration for high-dimensional robust regression, High-dimensional robust approximated \(M\)-estimators for mean regression with asymmetric data, Quantile regression feature selection and estimation with grouped variables using Huber approximation, Group penalized quantile regression, Unnamed Item, Unnamed Item, Adaptive elastic-net selection in a quantile model with diverging number of variable groups, Nonparametric estimation of conditional quantile functions in the presence of irrelevant covariates, Outlier detection and robust variable selection via the penalized weighted LAD-LASSO method, Unnamed Item, Estimation in quantile regression models for correlated data with diverging number of covariates and large cluster sizes

Cites Work

• Nearly unbiased variable selection under minimax concave penalty
• The Adaptive Lasso and Its Oracle Properties
• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• On M-processes and M-estimation
• Composite quantile regression and the oracle model selection theory
• One-step sparse estimates in nonconcave penalized likelihood models
• Limiting behavior of \(M\)-estimators of regression coefficients in high dimensional linear models. I: Scale-dependent case. II: Scale-invariant case
• On parameters of increasing dimensions
• The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems
• Nonconcave penalized likelihood with a diverging number of parameters.
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Forward Regression for Ultra-High Dimensional Variable Screening
• SparseNet: Coordinate Descent With Nonconvex Penalties
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Nonconcave Penalized Likelihood With NP-Dimensionality
• Smoothly Clipped Absolute Deviation on High Dimensions