MISSING DATA IMPUTATION USING WEIGHTED OF REGIME SWITCHING MEAN AND REGRESSION
- 1 Ubon Rachathani Rajabhat University, Thailand
- 2 , United Kingdom
Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. The purpose of this work were first to develop the Weighted of Regime Switching Mean and Regression (WRSMRI) for missing data estimation and secondly to compare its efficiency of estimation and statistical power of a test under Missing Complete At Random (MCAR) and simple random sampling with another methods, namely; Mean Imputation (MI) Regression Imputation (RI) Regime Switching Mean Imputation (RSMI) Regime Switching Regression Imputation (RSRI) and Average of Regime Switching Mean and Regression Imputation (ARSMRI). By using simulation data, the comparisons were made with the following conditions: (i) Three sample size (100, 200 and 500) (ii) three level of correlation of variables (low, moderate and high) and (iii) four level of percentage of missing data (5, 10, 15 and 20%). The best imputation under MSE and sample correlation estimated were obtained using WRSMRI method, under MAE MAPE power of the test sample mean and variance estimated were obtained using RSRI.
Copyright: © 2014 Jumlong Vongprasert and Bhusana Premanode. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Missing Data
- Regime Switching