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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Kannat Na Bangchang
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DOI:10.17265/2328-224X/2015.12.002
Thammasat University, Pathumthani, Thailand
This paper has compared variable selection method for multiple linear regression models that have both relative and non-relative variables in full model when predictor variables are highly correlated 0.999 . In this study two objective functions used in the Tabu Search are mean square error (MSE) and the mean absolute error (MAE). The results of Tabu Search are compared with the results obtained by stepwise regression method based on the hit percentage criterion. The simulations cover the both cases, without and with multicollinearity problems. For each situation, 1,000 iterations are examined by applying a different sample size n = 25 and 100 at 0.05 level of significance. Without multicollinearity problem, the hit percentages of the stepwise regression method and Tabu Search using the objective function of MSE are almost the same but slightly higher than the Tabu Search using the objective function of MAE. However with multicollinearity problem the hit percentages of the Tabu Search using both objective functions are higher than the hit percentage of the stepwise regression method.
Stepwise regression, Tabu Search, variable selection
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