Robust Estimation of Multi-response Surfaces Considering Correlation Structure

References

• Agullo J., Croux, C., Van Aelst, S. (2008). The multivariate least trimmed squares estimator. J. Multivariate Anal. 99:311–338.
   Web of Science ®Google Scholar
• Bashiri, M., Moslemi, A. (2011). A robust moving average iterative weighting method to analyze the effect of outliers on the response surface design. Int. J. Industr. Engi. Computat. 2:851–862.
   Google Scholar
• Bashiri, M., Moslemi A. (2013). The analysis of residuals variation and outliers to obtain robust response surface. J. Industr. Eng. Int.9:1–10.
   Google Scholar
• Cummins, DJ., Andrews C.W., (1995). Iteratively reweighted partial least squares: A performance analysis by Monte Carlo simulation. Journal of Chemometrics, 9:489–507.
   Web of Science ®Google Scholar
• Filzmoser, P., Todorov, V. (2011). Review of robust multivariate statistical methods in high dimension. Analytica Chimica Acta 705:2–14.
   PubMed Web of Science ®Google Scholar
• Filzmoser, P., Maronna, R., Werner, M., (2008). Outlier identification in high dimensions. Computat. Statist. Data Anal. 52:1694–1711.
   Web of Science ®Google Scholar
• Garcia Ben, M., Martinez, E., Yohai, VJ. (2006). Robust estimation for the multivariate linear model based on a τ-scale. J. Multivariate Anal. 97:1600–1622.
   Web of Science ®Google Scholar
• Huber, PJ. (1981) Robust Statistics. New York: John Wiley & Sons.
   Google Scholar
• Hubert, M., Rousseeuw, PJ., Van Aelst, S. (2008). High-breakdown robust multivariate methods. Statist. Sci. 23:92–119.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Khuri, A.I., Cornell, J.A. (1987). Response surfaces: Design and Analysis. New York: Marcel Dekker.
   Google Scholar
• Koenker, R., Portnoy, S. (1987). L-estimation for linear models. J. Amer. Statis. Assoc. 82:851–857.
   Web of Science ®Google Scholar
• Koksoy, O. (2008). A nonlinear programming solution to robust multi-response quality problem. Appl. Math. Computat. 196:603–612.
   Web of Science ®Google Scholar
• Maronna, RA., Martin, R.D., Yohai, V.J. (2006). Robust Statistics: Theory and Methods. New York: John Wiley and Sons.
   Google Scholar
• Maronna, RA., Morgenthaler, S. (1986). Robust regression through robust covariance. Commun. Statist.-Theor. Meth. 15:1347–1365.
   Web of Science ®Google Scholar
• Montgomery, DC. (2005). Introduction to Statistical Quality Control. 5th ed. New York: John Wiley & Sons.
   Google Scholar
• Morgenthaler, S., Schumacher, M.M. (1999). Robust analysis of a response surface design. Chemometr. Intell. Lab. Sys. 47:127–141.
   Web of Science ®Google Scholar
• Myers, R.H., Montgomery, D.C., Vining, G.G., Borror, C.M., Kowalski, SM., (2004). Response surface methodology: A retrospective and literature survey. J. Qual. Technol. 36:53–77.
   Web of Science ®Google Scholar
• Quesada G.M., Del Castillo, E., (2004). A dual-response approach to the multivariate robust parameter design problem. Technometrics 46:176–187.
   Web of Science ®Google Scholar
• Romano D., Varetto M., Vicario G., (2004). Multiresponse robust design: a general framework based on combined array. Journal of Quality Technology 36:27–37.
   Web of Science ®Google Scholar
• Rousseeuw P., Van Aelst, S., Van Driessen, K., Agullo J., (2004). Robust multivariate regression. Technometrics 46:293–305.
   Web of Science ®Google Scholar
• Taguchi, G. (1986). Introduction to Quality Engineering. White Plains, NJ.: Quality Resources.
   Google Scholar
• Weisberg S. (1985). Applied Linear Regression, 2nd ed. New York: John Wiley & Sons, Inc.
   Google Scholar
• Wiens, D., Wu, E.K. H. (2010). A comparative study of robust designs for M-estimated regression models. Computati. Statist. Data Anal. 54:1683–1695.
   Web of Science ®Google Scholar
• Wisnowskia, J.W., Montgomery, D.C., Simpson, J.R (2001). A comparative analysis of multiple outlier detection procedures in the linear regression model. Computat. Statist. Data Anal. 36:351–382.
   Web of Science ®Google Scholar
• Van Aelst, S., Willems, G. (2005). Multivariate regression S-estimators for robust estimation and inference. Statistica Sinica 15:981–1001.
   Web of Science ®Google Scholar
• Vuchkov, I., Boyadjieva, L. (2001). Quality improvement with design of experiment. In. Keller, A. Ed., The Netherlands: Kluwer Academic Publishers.
   Google Scholar