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ABSTRACT In this article, based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained, when the responses of linear models are… Click to show full abstract
ABSTRACT In this article, based on the covariate balancing propensity score (CBPS), estimators for the regression coefficients and the population mean are obtained, when the responses of linear models are missing at random. It is proved that the proposed estimators are asymptotically normal. In simulation studies and real example, the proposed estimators show improved performance relative to usual augmented inverse probability weighted estimators.
Keywords:
estimation linear;
cbps based;
based estimation;
missing random;
linear models
Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2018
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Journal Title: Communications in Statistics - Theory and Methods
Year Published: 2018
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!