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 ...

Imputation methods provide essential statistical tools for addressing missing data, thereby minimising bias and enhancing the reliability of parameter estimates. In statistical estimation, missing ...

Next, we will briefly cover early methods for handling missing data, such as complete case analysis and single imputation techniques (mean, hot deck, etc.), and why in practice they can produce ...

Missing data form an ubiquitous source of problems that most scientists or researchers cannot escape. For example, in survey applications, such as in social sciences or in official statistics, where ...

We develop an imputation method that uses the Dirichlet distribution to model the data. This method is convenient because of its flexibility. This procedure can impute data items that are non-negative ...