Imputation Strategy An overarching principal of multiple imputation is to model the response of interest, in this case the use of pesticides in the interim period between the administration of the ...

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 ...

Model averaging, specifically information theoretic approaches based on Akaike's information criterion (IT-AIC approaches), has had a major influence on statistical practices in the field of ecology ...

In this white paper, Bloomberg researchers show the applicability of deep latent variable models (DLVMs) in ESG datasets, outperforming classical imputation models as well as classical predictive ...

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 ...

Recent study focused on predicting short birth intervals (defined as less than 33 months) among reproductive-age women in ...