The ROBUSTREG Procedure


  • Akaike, H. (1974), “A New Look at the Statistical Model Identification,” IEEE Transactions on Automatic Control, AC-19, 716–723.

  • Brownlee, K. A. (1965), Statistical Theory and Methodology in Science and Engineering, New York: John Wiley & Sons.

  • Chen, C. (2002), “Robust Regression and Outlier Detection with the ROBUSTREG Procedure,” in Proceedings of the Twenty-Seventh Annual SAS Users Group International Conference, Cary, NC: SAS Institute Inc.

  • Chen, C. and Yin, G. (2002), “Computing the Efficiency and Tuning Constants for M-Estimation,” in Proceedings of the 2002 Joint Statistical Meetings, 478–482, Alexandria, VA: American Statistical Association.

  • Coleman, D. E., Holland, P. W., Kaden, N., Klema, V., and Peters, S. C. (1980), “A System of Subroutines for Iteratively Reweighted Least Squares Computations,” ACM Transactions on Mathematical Software, 6, 327–336.

  • Data and Story Library (2005), “Home Prices,” Accessed July 22, 2011.

  • De Long, J. B. and Summers, L. H. (1991), “Equipment Investment and Economic Growth,” Quarterly Journal of Economics, 106, 445–501.

  • Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., and Stahel, W. A. (1986), Robust Statistics: The Approach Based on Influence Functions, New York: John Wiley & Sons.

  • Hawkins, D. M., Bradu, D., and Kass, G. V. (1984), “Location of Several Outliers in Multiple Regression Data Using Elemental Sets,” Technometrics, 26, 197–208.

  • Holland, P. W. and Welsch, R. E. (1977), “Robust Regression Using Iteratively Reweighted Least-Squares,” Communications in Statistics—Theory and Methods, 6, 813–827.

  • Huber, P. J. (1973), “Robust Regression: Asymptotics, Conjectures, and Monte Carlo,” Annals of Statistics, 1, 799–821.

  • Huber, P. J. (1981), Robust Statistics, New York: John Wiley & Sons.

  • Marazzi, A. (1993), Algorithm, Routines, and S Functions for Robust Statistics, Pacific Grove, CA: Wadsworth & Brooks/Cole.

  • Ronchetti, E. M. (1985), “Robust Model Selection in Regression,” Statistics and Probability Letters, 3, 21–23.

  • Rousseeuw, P. J. (1984), “Least Median of Squares Regression,” Journal of the American Statistical Association, 79, 871–880.

  • Rousseeuw, P. J. and Hubert, M. (1996), “Recent Development in PROGRESS,” Computational Statistics and Data Analysis, 21, 67–85.

  • Rousseeuw, P. J. and Leroy, A. M. (1987), Robust Regression and Outlier Detection, New York: John Wiley & Sons.

  • Rousseeuw, P. J. and Van Driessen, K. (1999), “A Fast Algorithm for the Minimum Covariance Determinant Estimator,” Technometrics, 41, 212–223.

  • Rousseeuw, P. J. and Van Driessen, K. (2000), “An Algorithm for Positive-Breakdown Regression Based on Concentration Steps,” in W. Gaul, O. Opitz, and M. Schader, eds., Data Analysis: Scientific Modeling and Practical Application, 335–346, New York: Springer-Verlag.

  • Rousseeuw, P. J. and Yohai, V. (1984), “Robust Regression by Means of S-Estimators,” in J. Franke, W. Härdle, and R. D. Martin, eds., Robust and Nonlinear Time Series Analysis, number 26 in Lecture Notes in Statistics, 256–274, Berlin: Springer-Verlag.

  • Ruppert, D. (1992), “Computing S Estimators for Regression and Multivariate Location/Dispersion,” Journal of Computational and Graphical Statistics, 1, 253–270.

  • Yohai, V. J. (1987), “High Breakdown Point and High Efficiency Robust Estimates for Regression,” Annals of Statistics, 15, 642–656.

  • Yohai, V. J., Stahel, W. A., and Zamar, R. H. (1991), “A Procedure for Robust Estimation and Inference in Linear Regression,” in W. A. Stahel and S. W. Weisberg, eds., Directions in Robust Statistics and Diagnostics, New York: Springer-Verlag.

  • Yohai, V. J. and Zamar, R. H. (1997), “Optimal Locally Robust M-Estimates of Regression,” Journal of Statistical Planning and Inference, 64, 309–323.

  • Zaman, A., Rousseeuw, P. J., and Orhan, M. (2001), “Econometric Applications of High-Breakdown Robust Regression Techniques,” Econometrics Letters, 71, 1–8.