Penalized LAD Regression for Single-index Models

DOI10.1080/03610918.2014.904340zbMath1345.62105OpenAlexW2071045733MaRDI QIDQ2821006

Chaohui Guo, Jing Lv, Hu Yang

Publication date: 16 September 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2014.904340

zbMATH Keywords

variable selectionLASSOsingle-index modelLADlocal polynomial smoothing

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (1)

Sampling Lasso quantile regression for large-scale data

Cites Work

• Model-Free Feature Screening for Ultrahigh-Dimensional Data
• Single-index quantile regression
• Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression
• Penalized least squares for single index models
• Simultaneous estimation and variable selection in median regression using Lasso-type penalty
• One-step sparse estimates in nonconcave penalized likelihood models
• Sparse estimation and inference for censored median regression
• A nonlinear multi-dimensional variable selection method for high dimensional data: sparse MAVE
• Semiparametric least squares (SLS) and weighted SLS estimation of single-index models
• On the asymptotics of constrained \(M\)-estimation
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Feature Screening via Distance Correlation Learning
• An Adaptive Estimation of Dimension Reduction Space
• Local Linear Additive Quantile Regression
• Shrinkage Estimation of the Varying Coefficient Model

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