[1] Barnett, V. and Lewis, T. (1994) Outliers in Statistical Data. New York, Wiley.
[2] Belsley, D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics; Identifying Influence Data and Source of Collinearity. Wiley, New York. http://dx.doi.org/10.1002/0471725153
[3] Chatterjee, S. and Hadi, A.S. (1988) Sensitivity Analysis in Linear Regression. Wiley Series in Probability and Mathematical Statistics. Wiley, New York. http://dx.doi.org/10.1002/9780470316764
[4] Turkan, S., Meral, C.C. and Oniz, T. (2012) Outlier Detection by Regression Diagnostics Based on Robust Parameter Estimates. Hacettepe Journal of Mathematics and Statistics, 41, 147-155.
[5] Chen, C. (2002) Robust Regression and Outlier Detection with the ROBUSTREG Procedure. Proceedings of the Twenty-Seventh Annual SAS Users Group International Conference, SAS Institute Inc., Cary, NC.
[6] Gujarati, N.D. (2003) Basic Econometrics. 4th Edition, Tata McGraw-Hill, New Delhi, 748, 807
[7] Huber, P.J. (1973) Robust Regression: Asymptotics, Conjectures and Monte Carlo. Annals of Statistics, 1, 799-821. http://dx.doi.org/10.1214/aos/1176342503
[8] Rousseeuw, P.J. and Yohai, V. (1984) Robust Regression by Means of S Estimators in Robust and Nonlinear Time Series Analysis. In: Franke, J., Härdle, W. and Martin, R.D., Eds., Lecture Notes in Statistics, 26, Springer-Verlag, New York, 256-274.
[9] Rousseeuw, P.J. and Leroy, A.M. (1987) Robust Regression and Outlier Detection. Wiley Interscience, New York (Series in Applied Probability and Statistics), 329 pages. http://dx.doi.org/10.1002/0471725382
[10] Yohai, V.J. (1987) High Breakdown Point and High Efficiency Robust Estimates for Regression. Annals of Statistics, 15, 642-656. http://dx.doi.org/10.1214/aos/1176350366
[11] Rousseeuw, P.J. and van Driessen, K. (2006). Computing LTS Regression for Large Data Sets. Data Mining and Knowledge Discovery, 12, 29-45. http://dx.doi.org/10.1007/s10618-005-0024-4
[12] Cook, R.D. (1977) Detection of Influential Observations in Linear Regression. Technometrics, 19, 15-18. http://dx.doi.org/10.2307/1268249
[13] Michael, H.K., Christopher, J.N., John, N. and William L. (2005) Applied Linear Statistical Models. 5th Edition, New York, McGraw-Hill.