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CBPS-Based Inference in Nonlinear Regression Models with Missing Data
College of Applied Sciences, Beijing University of Technology, Beijing, China
School of Mathematics and Information Science, Shangqiu Normal University, Shangqiu, China
College of Applied Sciences, Beijing University of Technology, Beijing, China
- 1 College of Applied Sciences, Beijing University of Technology, Beijing, China
- 2 School of Mathematics and Information Science, Shangqiu Normal University, Shangqiu, China
- 3 College of Applied Sciences, Beijing University of Technology, Beijing, China
Open Journal of Statistics·Volume 06 (2016)·Pages 675–684·Published 22 July 2016·DOI10.4236/ojs.2016.64057
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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.
KeywordsNonlinear Regression ModelMissing at RandomCovariate Balancing Propensity ScoreGMMAugmented Inverse Probability Weighted
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