Multiple imputation is a principled approach to account for missing data in analyses where valid results depends on careful construction of the imputation model. The potential for misspecification of ...

Missing data are almost inevitable in medical research. This leads to a loss of power and potential bias. Multiple imputation is a widely-used and flexible approach for handling missing data. This ...

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's ...

We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS ...

In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...