Efficient inverse probability weighting method for quantile regression with nonignorable missing data

DOI10.1080/02331888.2016.1268615zbMath1371.62040OpenAlexW2565014989MaRDI QIDQ5280367

Depeng Jiang, Pu-Ying Zhao, Nian Sheng Tang

Publication date: 20 July 2017

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2016.1268615

zbMATH Keywords

quantile regressionempirical likelihoodauxiliary informationinverse probability weightingmissing not at random

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (9)

Robust statistical inference for longitudinal data with nonignorable dropouts ⋮ Bayesian semiparametric approach to quantile nonlinear dynamic factor analysis models with mixed ordered and nonignorable missing data ⋮ An efficient estimation for the parameter in additive partially linear models with missing covariates ⋮ Empirical likelihood in varying-coefficient quantile regression with missing observations ⋮ Statistical inferences for varying coefficient partially non linear model with missing covariates ⋮ Quantile Regression for Nonignorable Missing Data with Its Application of Analyzing Electronic Medical Records ⋮ Robust inference for estimating equations with nonignorably missing data based on SIR algorithm ⋮ Smoothed empirical likelihood inference and variable selection for quantile regression with nonignorable missing response ⋮ Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts

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