Abstract: Response surface methodology is a powerful tool for product/process improvement and optimization. In response surface modeling, the random errors are assumed to be normally distributed independent random variables with constant variance. However, due to the fact that outliers are inevitable in the observations, the constant variance assumption does not hold in practice. To dampen the effect of such observation random errors on the least square regression model, robust regression techniques are employed. Consider that the outlier which may occur in different experimental region and based on central composite design, performance analysis of reducing the influence of outliers for the Mestimators of robust regression is made. It includes three estimators: Huberestimator, Tukeyestimator, and Welschestimator. By comparison, it shows that Welsch and Tukeyestimators are better than Huberestimator in reducing the effect of outliers among response surface optimization and in response surface design. An example from chemical industry is used to calculate the optimal value of response surface model based on different experiment region of central composite design with and without outlier. In other words, the robust Mestimators, especially Welsch and Tukeyestimators, significantly improve the robustness of response surface modeling in large magnitude outliers (10 standard deviation).

Key words: response surface methodology, robust Mestimators, central composite design