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http://dx.doi.org/10.1198/016214506000000023

has been cited by the following article:

• TITLE: CBPS-Based Inference in Nonlinear Regression Models with Missing Data

  AUTHORS: Donglin Guo, Liugen Xue, Haiqing Chen

  KEYWORDS: Nonlinear Regression Model, Missing at Random, Covariate Balancing Propensity Score, GMM, Augmented Inverse Probability Weighted

  JOURNAL NAME: Open Journal of Statistics, Vol.6 No.4, August 25, 2016

  ABSTRACT: In this article, to improve the doubly robust estimator, the nonlinear regression models with missing responses are studied. Based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained. It is proved that the proposed estimators are asymptotically normal. In simulation studies, the proposed estimators show improved performance relative to usual augmented inverse probability weighted estimators.