Single Imputation Methods and Confidence Intervals for the Gini Index

The problem of missing data is a common feature in any study, and single imputation method often applied to deal with this problem. first contribution paper analyse the empirical performance some traditional methods when they are estimation Gini index, popular measure inequality used many studies. Various for constructing confidence intervals index also empirically evaluated. We consider several measures estimators intervals, allowing us quantify magnitude non-response bias find extremely large biases under certain mechanisms, gets noticeably worse as proportion increases. For correlation coefficient between target auxiliary variables, regression may notably mitigate problem, yielding appropriate mean square errors. that have poor coverage rates probability being not uniform, substantially improves handling