COMPARISON OF MISSING DATA ESTIMATION IN SIMPLE LINEAR REGRESSION BETWEEN SINGH AND EXPECTATION MAXIMIZATION ALGORITHM

Authors

  • Phitcha Khrueapaeng Graduate School, Chiang Mai University
  • Bandhita Plubin Department of Statistics, Faculty of Science, Chiang Mai University
  • Putipong Bookkamana
  • Manachai Rodchuen

Keywords:

Singh method, expectation maximization algorithm, root mean square error

Abstract

This study was focus on comparing the estimation methods for missing data in simple linear regression. The methods that used to estimate missing data are Singh method and Expectation Maximization Algorithm (EM). The comparison was done under condition of sample sizes 40, 100, 500 and 1,000; variances 1, 10 and 50; percentages of missing data 5%, 10% and 15%; the correlation coefficient levels between the dependent and independent variable are -0.3, -0.6, -0.9, 0.3, 0.6 and 0.9. The criterion of determination is Root Mean Square Error (RMSE). The results show that the EM method is a better estimation method than Singh method for simple linear regression due to EM method give the lowest RMSE values for all levels of correlation coefficients, sample sizes, variances and percentages of missing data.

References

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Published

2018-09-23

How to Cite

Khrueapaeng, P., Plubin, B., Bookkamana, P., & Rodchuen, M. (2018). COMPARISON OF MISSING DATA ESTIMATION IN SIMPLE LINEAR REGRESSION BETWEEN SINGH AND EXPECTATION MAXIMIZATION ALGORITHM. Life Sciences and Environment Journal, 19(2), 347–357. Retrieved from https://ph01.tci-thaijo.org/index.php/psru/article/view/122917

Issue

Vol. 19 No. 2 (2018): July - December

Section

Research Articles

License

Each article is copyrighted © by its author(s) and is published under license from the author(s).