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Measurement error and misclassification arise commonly in various data collection processes. It is well-known that ignoring these features in the data analysis usually leads to biased inference. With the generalized linear model setting, Yi et al. [Functional and structural methods with mixed measurement error and misclassification in covariates. J Am Stat Assoc. 2015;110:681–696] developed inference methods to adjust for the effects of measurement error in continuous covariates anddoi:10.6084/m9.figshare.8152493.v1 fatcat:rwpsdz44lfbvlnb3jwshnohcyy