COMPARATIVE STUDY OF SOME ESTIMATORS OF LINEAR REGRESSION MODELS IN THE PRESENCE OF OUTLIERS

Abstract

The paper examined the performance of five estimation methods using six different outlier percentages (0%, 5%, 10%, 20%, 30% and 40%) and five different sample sizes (20, 40, 60, 100 and 200) were used to investigate effect of sample size on the performance of each of the estimation methods. The study adopted absolute bias, variances, relative efficiency and root mean square errors as comparison criteria through Monte-Carlo experiment and real life data was used to validate the simulation results. The study found that, under 5%, 10%, 20% and 30% outlying condition Robust-MM is the most preferred estimator across all criteria and sample size except using relative efficiency criterion and when the sample size is 40, 200 and 200 under 5%, 20% and 30% outlying condition and using absolute bias criterion respectively while Robust-LTS is the least preferred estimator except when the sample size is 40, 20 ; 40, 20 ; 20, 200 under 5%, 20% and 30% outliers and using absolute bias, variance and root mean square error respectively. Under 40% outlying condition Robust-MM is the most preferred estimator across all criteria and sample size except using relative efficiency and when the sample size is 20. Furthermore, Robust-MM is the most consistent estimator across the comparison criteria except when using relative efficiency and sample size has little or no effect on the performance of the estimators across all the different outlier levels. R Statistical package was used for the data analysis. This study therefore recommends the used of Robust-MM estimator