Importance sampling imputation algorithms in quantile regression with their application in CGSS data

DOI10.1016/j.matcom.2021.04.014OpenAlexW3159024360MaRDI QIDQ2664818

Hao Cheng

Publication date: 18 November 2021

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.matcom.2021.04.014

zbMATH Keywords

importance samplingquantile regressioninverse probability weightingCGSS

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

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• Inverse probability weighted estimation for general missing data problems
• New imputation methods for missing data using quantiles
• On the convergence properties of the EM algorithm
• Semiparametric theory and missing data.
• Multiple imputation in quantile regression
• Estimating Equations Inference With Missing Data
• Regression Quantiles
• Weighted Semiparametric Estimation in Regression Analysis With Missing Covariate Data
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Quantile regression with covariates missing at random
• Quantile Regression With Measurement Error
• Efficient Quantile Regression Analysis With Missing Observations
• A Generalization of Sampling Without Replacement From a Finite Universe

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