A propensity score adjustment method for regression models with nonignorable missing covariates

DOI10.1016/j.csda.2015.07.017zbMath1468.62091OpenAlexW1435596204MaRDI QIDQ1660142

Depeng Jiang, Nian Sheng Tang, Pu-Ying Zhao

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.07.017

zbMATH Keywords

composite quantile regressionpropensity scorenot missing at randomexponentially tilted likelihood

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05) Nonparametric estimation (62G05)

Related Items

Statistical inference for nonignorable missing-data problems: a selective review, BAGEL: a non-ignorable missing value estimation method for mixed attribute datasets, A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse, Efficient inverse probability weighting method for quantile regression with nonignorable missing data, Weighted rank estimation for nonparametric transformation models with nonignorable missing data, Bayesian local influence analysis of general estimating equations with nonignorable missing data, Improved composite quantile regression and variable selection with nonignorable dropouts, Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses, Estimation of Conditional Prevalence From Group Testing Data With Missing Covariates, Estimation and test of restricted linear EV model with nonignorable missing covariates, Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data, Empirical likelihood weighted composite quantile regression with partially missing covariates, Weighted composite quantile regression analysis for nonignorable missing data using nonresponse instrument

Uses Software

• MICE

Cites Work

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• Large Sample Properties of Generalized Method of Moments Estimators
• Composite quantile regression and the oracle model selection theory
• Point estimation with exponentially tilted empirical likelihood
• Two step composite quantile regression for single-index models
• Limiting distributions for \(L_1\) regression estimators under general conditions
• Weak convergence and empirical processes. With applications to statistics
• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• Semiparametric theory and missing data.
• Using calibration weighting to adjust for nonresponse under a plausible model
• Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-ignorable
• Local Composite Quantile Regression Smoothing: An Efficient and Safe Alternative to Local Polynomial Regression
• An instrumental variable approach for identification and estimation with nonignorable nonresponse
• A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data
• Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood
• Empirical likelihood for estimating equations with nonignorably missing data
• Missing-Data Methods for Generalized Linear Models