Forward variable selection for ultra-high dimensional quantile regression models
From MaRDI portal
Publication:6046050
DOI10.1007/s10463-022-00849-zOpenAlexW3212645967MaRDI QIDQ6046050
Publication date: 15 May 2023
Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10463-022-00849-z
Related Items
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sure independence screening in generalized linear models with NP-dimensionality
- Nearly unbiased variable selection under minimax concave penalty
- A stepwise regression method and consistent model selection for high-dimensional sparse linear models
- Statistics for high-dimensional data. Methods, theory and applications.
- Forward variable selection for sparse ultra-high-dimensional generalized varying coefficient models
- Globally adaptive quantile regression with ultra-high dimensional data
- A selective overview of feature screening for ultrahigh-dimensional data
- Weak convergence and empirical processes. With applications to statistics
- Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
- Screening and selection for quantile regression using an alternative measure of variable importance
- Perturbation bootstrap in adaptive Lasso
- Adaptively weighted group Lasso for semiparametric quantile regression models
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Adaptive robust variable selection
- Extended BIC for small-n-large-P sparse GLM
- Forward Regression for Ultra-High Dimensional Variable Screening
- Extended Bayesian information criteria for model selection with large model spaces
- Regression Quantiles
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Model Selection via Bayesian Information Criterion for Quantile Regression Models
- Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space
- CONDITIONAL MARGINAL TEST FOR HIGH DIMENSIONAL QUANTILE REGRESSION
- Statistical Foundations of Data Science
- Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach
- Conditional quantile screening in ultrahigh-dimensional heterogeneous data
- Partially linear additive quantile regression in ultra-high dimension
