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.