designed to work with complete datasets. Data imputation (DI) assists in creating a comprehensive representation of the data … imputation technique is essential in minimizing the errors and biases introduced in the data during the imputation. The … difficulty in creating an imputation method that performs optimally across the entire spectrum of data stems from the disparity …

We propose an observation-driven dynamic common factor model for missing value imputation in high-dimensional panel … point forecasts, but also density forecasts for the missing data, thus allowing the researcher to quantify imputation …

The prevailing consensus in statistical literature is that multiple imputation is generally the most suitable method … interactions between the imputation method, missingness mechanism, and missingness rate. Furthermore, we examine the time … efficiency of these "state-of-the-art" imputation methods considering the time-sensitive nature of certain machine learning …

. Findings are compared with particular cases and confidence intervals are used as a basic tool of comparison. Pattern imputation …

-SILC) microdata which contains detailed socio-demographic and socio-economic information. The imputation process employs a statistical … imputation performance in distributional terms and the macro validation of the resulting datasets are thoroughly examined. We …

We propose an observation-driven dynamic common factor model for missing value imputation in high-dimensional panel … point forecasts, but also density forecasts for the missing data, thus allowing the researcher to quantify imputation …