title: Estimation of mean response via effective balancing score.
abstract: We introduce effective balancing scores for estimation of the mean response under a missing at random mechanism. Unlike conventional balancing scores, the effective balancing scores are constructed via dimension reduction free of model specification. Three types of effective balancing scores are introduced: those that carry the covariate information about the missingness, the response, or both. They lead to consistent estimation with little or no loss in efficiency. Compared to existing estimators, the effective balancing score based estimator relieves the burden of model specification and is the most robust. It is a near-automatic procedure which is most appealing when high dimensional covariates are involved. We investigate both the asymptotic and the numerical properties, and demonstrate the proposed method in a study on Human Immunodeficiency Virus disease.
authors: Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Maryland 20892-7609, USA.
authors: Zonghui
authors: Z
authors: Hu
authors: Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Maryland 20892-7609, USA.
authors: Dean A
authors: DA
authors: Follmann
authors: Department of Statistics, University of Michigan, Ann Arbor MI 48109-1107, USA.
authors: Naisyin
authors: N
authors: Wang
keywords: Balancing score; Dimension reduction; Missing at random; Nonparametric kernel regression; Prognostic score; Propensity score