Author(s)

Donglin Guo^1,2, Liugen Xue², Haiqing Chen¹

¹College of Applied Sciences, Beijing University of Technology, Beijing, China.
²School of Mathematics and Information Science, Shangqiu Normal University, Shangqiu, China.

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.

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

Guo, D. , Xue, L. and Chen, H. (2016) CBPS-Based Inference in Nonlinear Regression Models with Missing Data. Open Journal of Statistics, 6, 675-684. doi: 10.4236/ojs.2016.64057.