scientific article; zbMATH DE number 7306908
From MaRDI portal
Publication:5149019
Yan Yu, Heng Lian, Yuankun Zhang
Publication date: 5 February 2021
Full work available at URL: https://jmlr.csail.mit.edu/papers/v21/19-173.html
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Related Items (4)
Single-index composite quantile regression for ultra-high-dimensional data ⋮ Single-index relative error regression models ⋮ Empirical likelihood in single-index quantile regression with high dimensional and missing observations ⋮ New estimation for heteroscedastic single-index measurement error models
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Estimation and testing for partially linear single-index models
- Nearly unbiased variable selection under minimax concave penalty
- Single-index quantile regression
- The Adaptive Lasso and Its Oracle Properties
- Inference for single-index quantile regression models with profile optimization
- The EFM approach for single-index models
- Estimation and variable selection for generalized additive partial linear models
- Fast global convergence of gradient methods for high-dimensional statistical recovery
- Conditional growth charts. (With discussion and rejoinder)
- Quantile regression in partially linear varying coefficient models
- Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
- Additive regression and other nonparametric models
- Slicing regression: A link-free regression method
- Semiparametric least squares (SLS) and weighted SLS estimation of single-index models
- Convex analysis approach to d. c. programming: Theory, algorithms and applications
- Partially linear modeling of conditional quantiles using penalized splines
- A globally convergent algorithm for nonconvex optimization based on block coordinate update
- Estimation and variable selection for quantile partially linear single-index models
- A dimension reduction based approach for estimation and variable selection in partially linear single-index models with high-dimensional covariates
- Spline estimation and variable selection for single-index prediction models with diverging number of index parameters
- High dimensional single index models
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Adaptive robust variable selection
- Strong oracle optimality of folded concave penalized estimation
- L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
- Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
- Convergence rate of b-spline estimators of nonparametric conditional quantile functions∗
- Can Tests for Jumps be Viewed as Tests for Clusters?
- Regularized quantile regression and robust feature screening for single index models
- Investigating Smooth Multiple Regression by the Method of Average Derivatives
- Sliced Inverse Regression for Dimension Reduction
- Regression Quantiles
- Generalized Partially Linear Single-Index Models
- Estimation in a semiparametric model for longitudinal data with unspecified dependence structure
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Penalized Spline Estimation for Partially Linear Single-Index Models
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- An Adaptive Estimation of Dimension Reduction Space
- Semiparametric Estimation of Index Coefficients
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Model Selection via Bayesian Information Criterion for Quantile Regression Models
- Multiple Quantile Modelling via Reduced Rank Regression
- High-Dimensional Variable Selection With Reciprocal L1-Regularization
- Sparse single-index model
- Identifiability of single-index models and additive-index models
- Partially linear additive quantile regression in ultra-high dimension
This page was built for publication:
