Research Article Open Access

MISSING DATA IMPUTATION USING WEIGHTED OF REGIME SWITCHING MEAN AND REGRESSION

Jumlong Vongprasert1 and Bhusana Premanode2
  • 1 Ubon Rachathani Rajabhat University, Thailand
  • 2 , United Kingdom

Abstract

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.

Journal of Mathematics and Statistics
Volume 10 No. 2, 2014, 255-261

DOI: https://doi.org/10.3844/jmssp.2014.255.261

Submitted On: 16 November 2013 Published On: 3 May 2014

How to Cite: Vongprasert, J. & Premanode, B. (2014). MISSING DATA IMPUTATION USING WEIGHTED OF REGIME SWITCHING MEAN AND REGRESSION. Journal of Mathematics and Statistics, 10(2), 255-261. https://doi.org/10.3844/jmssp.2014.255.261

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Keywords

  • Missing Data
  • Imputation
  • Regime Switching