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

Depeng Jiang, Puying Zhao and Niansheng Tang

Computational Statistics & Data Analysis, 2016, vol. 94, issue C, 98-119

Abstract: In a linear regression model with nonignorable missing covariates, non-normal errors or outliers can lead to badly biased and misleading results with standard parameter estimation methods built on either least squares- or likelihood-based methods. A propensity score method with a robust and efficient regression procedure called composite quantile regression for parameter estimation of the linear regression model with nonignorable missing covariates is proposed. Semiparametric estimation of the propensity score is based on the exponentially tilted likelihood approach. Asymptotic properties of the proposed estimators are systematically investigated. The proposed method is resistant to heavy-tailed errors or outliers in the response. Simulation studies and real data applications are used to illustrate its potential impacts and benefits compared with conventional methods.

Keywords: Exponentially tilted likelihood; Composite quantile regression; Not missing at random; Propensity score (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:94:y:2016:i:c:p:98-119

DOI: 10.1016/j.csda.2015.07.017

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