This article considers misclassification of categorical covariates in the context of regression analysis; if unaccounted for, such errors usually result in mis‐estimation of model parameters. With the presence of additional covariates, we exploit the fact that explicitly modelling non‐differential misclassification with respect to the response leads to a mixture regression representation. Under the framework of mixture of experts, we enable the reclassification probabilities to vary with other covariates, a situation commonly caused by misclassification that is differential on certain covariates and/or by dependence between the misclassified and additional covariates. Using Bayesian inference, the mixture approach combines learning from data with external information on the magnitude of errors when it is available. In addition to proving the theoretical identifiability of the mixture of experts approach, we study the amount of efficiency loss resulting from covariate misclassification and the usefulness of external information in mitigating such loss. The method is applied to adjust for misclassification on self‐reported cocaine use in the Longitudinal Studies of HIV‐Associated Lung Infections and Complications.

中文翻译：

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贝叶斯专家混合方法进行协变量错误分类

本文考虑了回归分析中分类协变量的错误分类；如果无法解释，则此类错误通常会导致对模型参数的错误估计。在存在其他协变量的情况下，我们利用以下事实：对响应进行显式建模的非微分错误分类会导致混合回归表示。在专家的混合框架下，我们使重新分类的概率能够随其他协变量而变化，这种情况通常是由某些协变量之间存在差异的错误分类和/或错误分类的和其他协变量之间的依赖性导致的。使用贝叶斯推理，混合方法将数据学习与有关错误程度的外部信息（如果可用）结合在一起。除了证明专家混合方法的理论可识别性之外，我们还研究了因协变量错误分类而导致的效率损失的数量以及外部信息在减轻此类损失方面的有用性。该方法适用于在与HIV相关的肺部感染和并发症的纵向研究中对自我报告的可卡因使用情况进行错误分类调整。