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

descriptionPublicationkeyboard_double_arrow_right Article 01 Feb 2016 English Publisher:Elsevier BVJournal:Computational Statistics & Data Analysis, volume 94, pages 98-119 (issn: 0167-9473,

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Authors: Depeng Jiang; Pu-Ying Zhao; Nian-Sheng Tang;

doi: 10.1016/j.csda.2015.07.017

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

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Abstract

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Related Organizations

University of Manitoba
Canada
Yunnan Open University
China (People's Republic of)

Keywords

Linear regression; mixed models, exponentially tilted likelihood, composite quantile regression, Nonparametric regression and quantile regression, not missing at random, Computational methods for problems pertaining to statistics, Nonparametric estimation, Asymptotic properties of parametric estimators, propensity score

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