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Proxies are usually only sought to report on behalf of participants with missing self\u2010reports; thus, either a participant self\u2010report or proxy report, but not both, is available for each participant. Furthermore, the missing\u2010data mechanism for participant self\u2010reports is not identifiable and may be nonignorable. When exposures are binary and participant self\u2010reports are conceptualized as the gold standard, substituting error\u2010prone proxy reports for missing participant self\u2010reports may produce biased estimates of outcome means. Researchers can handle this data structure by treating the problem as one of misclassification within the stratum of participants with missing self\u2010reports. Most methods for addressing exposure misclassification require validation data, replicate data, or an assumption of nondifferential misclassification; other methods may result in an exposure misclassification model that is incompatible with the analysis model. We propose a model that makes none of the aforementioned requirements and still preserves model compatibility. Two user\u2010specified tuning parameters encode the exposure misclassification model. Two proposed approaches estimate outcome means standardized for (potentially) high\u2010dimensional covariates using multiple imputation followed by propensity score methods. The first method is parametric and uses maximum likelihood to estimate the exposure misclassification model (i.e., the imputation model) and the propensity score model (i.e., the analysis model); the second method is nonparametric and uses boosted classification and regression trees to estimate both models. We apply both methods to a study of elderly hip fracture patients. 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