Inverse probability weighting (IPW) estimates are widely used in applications where data are missing due to nonresponse or censoring or in observational studies of causal effects where the counterfactuals cannot be observed. This extensive literature has shown the estimators to be consistent and asymptotically normal under very general conditions, and combining IPW with modeling for the mean function yields "doubly robust" estimates which are consistent and asymptotically normal. Recent studies have considered estimation of the response or treatment assignment functions and have shown nonparametric and boosting type estimators work well in simulations and applications. The purposes of this study are to: (1) Develop an analytic form for a weighting function that can be used to estimate weighted means that consistently estimate the population mean from incomplete data when covariates are measured with error; (2) Develop a consistent estimator of the weighting function and the population mean when covariates are measured with error; and (3) Evaluate the small sample properties of the estimator through a simulation study.