Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145479 | Journal of Multivariate Analysis | 2015 | 12 Pages |
Abstract
In longitudinal data analysis with dropouts, despite its local efficiency in theory, the augmented inverse probability weighted (AIPW) estimator hardly achieves the semiparametric efficiency bound in practice, even if the variance–covariance of the longitudinal outcomes is correctly modeled. In this paper, we propose a method based on conditional empirical likelihood. Assuming missing at random (MAR) mechanism, our estimator is doubly robust and locally efficient. Unlike the AIPW estimator, our estimator does not require to model any second moments, including the variance–covariance of the longitudinal outcomes, in order to achieve the semiparametric efficiency bound.
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Peisong Han, Peter X.-K. Song, Lu Wang,