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CBPS-Based Inference in Nonlinear Regression Models with Missing Data

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DOI: 10.4236/ojs.2016.64057    1,240 Downloads   1,596 Views

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.

Cite this paper

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.

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