Achieving semiparametric efficiency bound in longitudinal data analysis with dropouts

Peisong Han, Peter X.-K. Song and Lu Wang

Journal of Multivariate Analysis, 2015, vol. 135, issue C, 59-70

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.

Keywords: Augmented inverse probability weighting (AIPW); Double robustness; Empirical likelihood; Local efficiency; Missing at random (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:135:y:2015:i:c:p:59-70

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DOI: 10.1016/j.jmva.2014.12.006

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