CBPS-based estimation for linear models with responses missing at random

Donglin Guo and Liugen Xue

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 17, 4160-4169

Abstract: In this article, based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained, when the responses of linear models are missing at random. It is proved that the proposed estimators are asymptotically normal. In simulation studies and real example, the proposed estimators show improved performance relative to usual augmented inverse probability weighted estimators.

Date: 2018
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DOI: 10.1080/03610926.2017.1371752

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