Penalized inverse probability weighted estimators for weighted rank regression with missing covariates

DOI10.1080/03610926.2013.863930zbMath1341.62139OpenAlexW1972478446MaRDI QIDQ2807773

Jing Lv, Chaohui Guo, Hu Yang

Publication date: 25 May 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.863930

zbMATH Keywords

variable selectionHorvitz-Thompson propertySCADMARrank-based analysis

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Rank-based inference for the single-index model
• Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression
• One-step sparse estimates in nonconcave penalized likelihood models
• Generalized partially linear models with missing covariates
• A weighted dispersion function for estimation in linear models
• Rank-based variable selection
• Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
• Inference and missing data
• Estimating Regression Coefficients by Minimizing the Dispersion of the Residuals
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Nonparametric Estimate of Regression Coefficients
• Linear Statistical Inference and its Applications
• Estimation in Partially Linear Models With Missing Covariates

This page was built for publication: Penalized inverse probability weighted estimators for weighted rank regression with missing covariates