Efficiency Comparison of Regression Coefficient Estimation Methods for Multiple Linear Regression Model when Data Contain Outliers in Dependent Variable

Kritaporn Thitacharee, Juthaphorn Sinsomboonthong, Thidaporn Supapakorn

Abstract


The purpose of this research was to compare the efficiency of five regression coefficient estimation methods for multiple linear regression model when data containing mild outliers in dependent  variable. The                    five  methods  composed  of  ordinary least  squares method, least  trimmed  squares  method,  M  method  using

Andrews and Welsch weight functions and GM method using Huber weight function. The criterion for efficiency comparison was estimated mean square error (EMSE). The data was generated by Monte Carlo simulation technique for 78 situations and repeated 1,000 times for each situation. The results of this research were as follow: in case of no outliers in dependent variable, ordinary least squares method was the most efficient method. In case of outliers in dependent variable and random error was normally distributed, when sample size was 10, 20 and 30, M method using Welsch weight function provided the most efficient estimator. In addition, when sample size was 50, 100 and 150, M method using Andrews weight function provided the most efficient estimator. However, when random error was t-distributed with 1 degree of freedom, GM tended to be the most efficient estimator for all situations. Moreover, when degree of freedom increased and sample size was not greater than 30, M method using Welsch weight function was likely to be the most efficient estimator. However, when sample size was greater than 30, M method using Andrews weight function tended to be the most efficient estimator.

 

Keywords :  multiple linear regression model, outliers, regression coefficient, ordinary least squares method,                                mean square error


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References


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