How to cite this paper
Bashiri, M & Moslemi, A. (2011). A robust moving average iterative weighting method to analyze the effect of outliers on the response surface design.International Journal of Industrial Engineering Computations , 2(4), 851-862.
Refrences
Bashiri, M., Hejazi, T.H, & Badri, H. (2009). Multiple simulation response surfaces for robust optimization in inventory system, 6th International Industrial Engineering Conference. Tehran
Bertsimas, D. & Shioda, R. (2007).Classification and regression via integer optimization. Operations Research, 55, 252-271.
Bickela, D. R. & Frühwirthb, R. (2006). On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications. Computational Statistics & Data Analysis, 50, 3500-3530.
Cummins, D.J. & Andrews, C.W. (1995).Iteratively reweighted partial least squares: A performance analysis by Monte Carlo simulation. Journal of Chemometrics, 9, 489-507.
Hejazi, T. H., Bashiri, M., Noghondarian, K. & Atkinson, A.C. (2010). Multiresponse optimization with consideration of probabilistic covariates. Quality and Reliability Engineering International, DOI: 10.1002/qre.1133.
Dornheim, H. & Brazauskas, V. (2011). Robust-efficient fitting of mixed linear models: Methodology and theory. Journal of Statistical Planning and Inference, 141, 1422–1435.
Huber, P.J. (1981). Robust Statistics.New York: John Wiley & Sons.
Hund, E., Massart, D. L. & Smeyers-Verbeke, J. (2002). Robust regression and outlier detection in the evaluation of robustness tests with different experimental designs. Analytica Chimica Acta, 463, 53–73.
Kazemzadeh, R. B., Bashiri, M., Atkinson, A. C. & Noorossana, R. (2008). A general framework for multiresponse optimization problems based on goal programming. European Journal of Operational Research, 189, 421-429.
Maronna, R. A., Martin, R. D. & Yohai, V. J. (2006). Robust statistics: Theory and Methods. New York: John Wiley and Sons.
Massart, D. L., Kaufman, L., Rousseeuw, P. J. & Leroy, A. (1986). Least median of squares: a robust method for outlier and model error detection in regression and calibration, AnalyticaChimica Acta,187, 171-179.
Morgenthaler, S. & Schumacher, M.M. (1999). Robust analysis of a response surface design. Chemometrics and intelligent laboratory systems, 47, 127-141.
Nguyena, T. D. & Welsch, R. (2010). Outlier detection and least trimmed squares approximation usingsemi-definite programming. Computational Statistics and Data Analysis, 54, 3212-3226.
Ortiz, M. C., Sarabia, L. A. & Herrero, A. (2006). Robust regression techniques A useful alternative for detection of outlier data. Talanta, 70, 499-512.
Pop, H. F. & Sârbu, C. (1996). A New Fuzzy Regression Algorithm. Analytical Chemistry, 68, 771-778.
Rousseeuw, P. J. (1984). Least median of squares regression, Journal of the American statistical association, 79, 871-880.
Rousseeuw, P. J. & Leroy, A. M. (1987). Robust regression and outlier detection. New York: John Wiley and Sons.
Rousseeuw, P. J., van Driessen, K., (2006). Computing LTS regression for large data sets. Data Mining and Knowledge Discovery, 12, 29-45.
Serneels, S., Croux, C., Filzmoser, P. &. Van Espen, P.J. (2005). Partial robust M-regression, Chemometrics and Intelligent Laboratory Systems, 79, 55-64.
Siegel, A. F. (1982). Robust regression using repeated medians. Biometrika, 69, 242-244.
Wiensa, D. P. & Wu, E. K. H., (2010). A comparative study of robust designs for M-estimated regression models. Computational Statistics and Data Analysis, 54, 1683-1695.
Wisnowskia, J. W., Montgomery D. C. & Simpson, J. R. (2001). A Comparative analysis of multipleoutlier detection procedures in thelinear regression model. Computational Statistics & Data Analysis, 36, 351-382.
Zioutas, G., & Avramidis, A. (2005). Deleting outliers in robust regression with mixed integer programming. Acta Mathematicae Applicatae Sinica, 21, 323-334.
Bertsimas, D. & Shioda, R. (2007).Classification and regression via integer optimization. Operations Research, 55, 252-271.
Bickela, D. R. & Frühwirthb, R. (2006). On a fast, robust estimator of the mode: Comparisons to other robust estimators with applications. Computational Statistics & Data Analysis, 50, 3500-3530.
Cummins, D.J. & Andrews, C.W. (1995).Iteratively reweighted partial least squares: A performance analysis by Monte Carlo simulation. Journal of Chemometrics, 9, 489-507.
Hejazi, T. H., Bashiri, M., Noghondarian, K. & Atkinson, A.C. (2010). Multiresponse optimization with consideration of probabilistic covariates. Quality and Reliability Engineering International, DOI: 10.1002/qre.1133.
Dornheim, H. & Brazauskas, V. (2011). Robust-efficient fitting of mixed linear models: Methodology and theory. Journal of Statistical Planning and Inference, 141, 1422–1435.
Huber, P.J. (1981). Robust Statistics.New York: John Wiley & Sons.
Hund, E., Massart, D. L. & Smeyers-Verbeke, J. (2002). Robust regression and outlier detection in the evaluation of robustness tests with different experimental designs. Analytica Chimica Acta, 463, 53–73.
Kazemzadeh, R. B., Bashiri, M., Atkinson, A. C. & Noorossana, R. (2008). A general framework for multiresponse optimization problems based on goal programming. European Journal of Operational Research, 189, 421-429.
Maronna, R. A., Martin, R. D. & Yohai, V. J. (2006). Robust statistics: Theory and Methods. New York: John Wiley and Sons.
Massart, D. L., Kaufman, L., Rousseeuw, P. J. & Leroy, A. (1986). Least median of squares: a robust method for outlier and model error detection in regression and calibration, AnalyticaChimica Acta,187, 171-179.
Morgenthaler, S. & Schumacher, M.M. (1999). Robust analysis of a response surface design. Chemometrics and intelligent laboratory systems, 47, 127-141.
Nguyena, T. D. & Welsch, R. (2010). Outlier detection and least trimmed squares approximation usingsemi-definite programming. Computational Statistics and Data Analysis, 54, 3212-3226.
Ortiz, M. C., Sarabia, L. A. & Herrero, A. (2006). Robust regression techniques A useful alternative for detection of outlier data. Talanta, 70, 499-512.
Pop, H. F. & Sârbu, C. (1996). A New Fuzzy Regression Algorithm. Analytical Chemistry, 68, 771-778.
Rousseeuw, P. J. (1984). Least median of squares regression, Journal of the American statistical association, 79, 871-880.
Rousseeuw, P. J. & Leroy, A. M. (1987). Robust regression and outlier detection. New York: John Wiley and Sons.
Rousseeuw, P. J., van Driessen, K., (2006). Computing LTS regression for large data sets. Data Mining and Knowledge Discovery, 12, 29-45.
Serneels, S., Croux, C., Filzmoser, P. &. Van Espen, P.J. (2005). Partial robust M-regression, Chemometrics and Intelligent Laboratory Systems, 79, 55-64.
Siegel, A. F. (1982). Robust regression using repeated medians. Biometrika, 69, 242-244.
Wiensa, D. P. & Wu, E. K. H., (2010). A comparative study of robust designs for M-estimated regression models. Computational Statistics and Data Analysis, 54, 1683-1695.
Wisnowskia, J. W., Montgomery D. C. & Simpson, J. R. (2001). A Comparative analysis of multipleoutlier detection procedures in thelinear regression model. Computational Statistics & Data Analysis, 36, 351-382.
Zioutas, G., & Avramidis, A. (2005). Deleting outliers in robust regression with mixed integer programming. Acta Mathematicae Applicatae Sinica, 21, 323-334.