Sequential Bayesian Regression for Multiple Imputation and Conditional Editing

TL;DRAbstract

Analysts faced with errors in data apply editing rules to fix erroneous data. These edits are deterministically assigned and edits may not be correct in all cases. This dissertation presents a unified method to multiply impute missing data and multiply edit erroneous data using a sequence of Bayesian regression models. The techniques used to multiply edit erroneous data are an exact parallel for multiple imputation used to correct missing data. The models presented allow for different data types subject to several error mechanisms. This method is called Sequential Bayesian Regression for Multiple Imputation and Conditional Editing (SyBRMICE) and creates multiple fully imputed and edited data sets. Desired analyses are performed on each complete and consistently edited and imputed data set individually. Results from these analyses are combined using the same combining rules used in multiple imputation. The resulting parameter estimates and intervals will then correctly account for the e

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