___

• Belsley, D.A. , Kuh, E. Welsch, R., “Regression Diagnostics”. Wiley and Sons, N.Y. and Toronto. (1980).
• Billor, N., Chatterjee, S., Hadi, A.S., “A Re- Weighted Least Squares Method for Robust Regression Estimation”, American Journal of Mathematical and Management Sciences, 26, Nos. 3&4, 229-252 (2006).
• Billor, N., Kiral, G., “A comparison of multiple outlier detection methods for regression data”, Comm. in Stat., 37, 521-545 (2008).
• Cook, R.D., Weisberg, S., “Residuals and Influence in Regression. London: Chapman and Hall (1982).
• Hadi, A.S., “Identifying multiple outliers in multivariate data”, J. Roy. Statist. Soc. Ser. B 54, 761–777 (1992).
• Hadi, A.S., Simonoff, J.S., Procedures for the identification of multiple outliers in linear models, J. Amer. Statist. Assoc., 88, 1264 –1272 (1993).
• Hadi, A.S., “A modification of a method for the detection of outliers in multivariate samples”, J. Roy. Statist. Soc. Ser. B 56, 393–396 (1994).
• Kianifard, F., Swallow, W., A Monte Carlo comparison of some procedures for identifying outliers in linear regression, Commun. Statist. Part A Theory Methods, 19, 1913–1938 (1990).
• Kim, S., Krzanowski, W.J., , Detecting multiple outliers in linear regression using a cluster method combined with graphical visualization, Comp. Stat., 22, 109-119 2007
• Marchette, D.J., Solka, J.L., 2003, Using Data Images for Outlier Detection, Comput. Statist. Data Anal., 43, 541-552.
• McCullough, B. D., 1998 Assessing the reliability of statistical software: Part I. The American Statistician 52, 358–366.
• McCullough, B. D., 1999, “Assessing the reliability of statistical software: Part II”. The American Statistician 53, 149-159.
• McCullough, B. D., 2000,The Accuracy of Mathematica 4 as a Statistical Package,, Computational Statistics 15 , 279-299.
• McCullough, B. D. , Heiser, D. A., 2008, On the accuracy of statistical procedures in Microsoft Excel 2007, Computational Statistics and Data Analysis 52, 4570-4578.
• Pena, D., Yahoi, V.J., 1995, The detection of Influential Subsets in Linear Regression by Using an Influence Matrix, J. Roy. Statist. Soc. Ser. B 57(1), 145-156.
• Rousseeuw, P.J, 1984, Least median of squares regression, J. Amer. Statist. Assoc. 79, 871-881.
• Rousseeuw,P.J, 1985,Multivariate Estimation with
• High Breakdown Point, Mathematical Statistics and Applications, ed. by W. Grossmann, G. Pflug, I. Vincze, and W. Wertz, Dordrecht: Reidel Publishing Company, 283-297.
• Rousseeuw, P.J., Leroy, A.M., 1987, “Robust Regression and Outlier Detection”, Wiley, New York.
• Rousseeuw, P.J, Van Zomeren, B.C., 1990, Unmasking multivariate outliers and leverage points, J. Amer. Statist. Assoc., 85, 633-639.
• Rousseeuw,P.J, Huber,M,1997, URL. http://www.agoras.ua.ac.be/abstract/Recdev97.htm
• Sebert, D.M., Montgomery, D.C., Rollier, D., 1998, A clustering algorithm for identifying multiple outliers in linear regression, Comput. Statist. Data Anal. 27, 461-484.
• Wisnowski, J.W. , Montgomery, D.C., Simpson, J.R., 2001, “A comparative analysis of multiple outlier detection procedures in the linear regression model”, Comput. Statist. Data Anal., 36, 351–382.