Nonparametric Regression With Missing Outcomes Using Weighted Kernel Estimating Equations.

ABSTRACT: We consider nonparametric regression of a scalar outcome on a covariate when the outcome is missing at random (MAR) given the covariate and other observed auxiliary variables. We propose a class of augmented inverse probability weighted (AIPW) kernel estimating equations for nonparametric regression under MAR. We show that AIPW kernel estimators are consistent when the probability that the outcome is observed, that is, the selection probability, is either known by design or estimated under a correctly specified model. In addition, we show that a specific AIPW kernel estimator in our class that employs the fitted values from a model for the conditional mean of the outcome given covariates and auxiliaries is double-robust, that is, it remains consistent if this model is correctly specified e

SUBMITTER: Wang L 

PROVIDER: S-EPMC3491912 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature