Estimating Missing Data with Bayes Bootstrap Regression Imputation
Abstract
This research is about estimating missing data when dependent variable Y is correlated with independent variable X, and X and Y are distributed as normal. The proposed method for estimating missing data is Bayes-Bootstrap regression imputation method (BRI) that is compared with regression imputation method (RI) and distance regression imputation method (DRI). The measurement criteria is mean absolute error (MAE). Comparing of estimating missing data used the Monte Carlo simulation technique. The results of study indicate that BRI and RI are more accuracy than DRI for all cases, but BRI presents the lowest mean absolute error in some case. Therefore, researchers introduce the BRI method for estimating missing data when the correlation coefficient between dependent variable Y and independent variable X is known and both variable distributions are normal distributions.
Keywords : : missing data ; Bayes-Bootstrap regression imputation ; Distance regression imputation ;
regression imputation
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