REFERENCES
- Belsley , D. A. , Kuh , E. and Welsch , R. 1980 . Regression Diagnostics: Identifying Influential Data and Sources of Collinearity New York : John Wiley. .
- Bickel , P. J. 1982 . “Robust Regression Based on Infinitesimal Neighborhoods,” Berkeley , CA : Department of Statistics, University of California. . Research Report 16
- David , H. A. 1970 . Order Statistics New York : John Wiley. .
- Dennis , J. E. , Gay , D. M. and Welsch , R. E. 1981 . “An Adaptive Nonlinear Least-Squares Algorithm,” . ACM Transactions on Mathematical Software , 7 : 348 – 383 .
- Donoho , D. L. and Huber , P. J. 1983 . “The Notion of Breakdown Point,”. ” . In A Festschrift for Erick L. Lehmann Edited by: Bickel , P. J. , Doksum , K. A. and Hodges , J. L. 157 – 184 .
- Friedman , J. H. and Stuetzle , W. 1981 . “Projection Pursuit Regression,” . Journal of the American Statistical Association , 76 : 817 – 823 .
- Huber , P. J. 1983 . “Minimax Aspects of Bounded Influence Regression,” . Journal of the American Statistical Association , 78 : 66 – 72 .
- Krasker , W. S. and Welsch , R. E. 1982 . “Efficient Bounded-Influence Regression Estimation,” . Journal of the American Statistical Association , 77 : 595 – 604 .
- Krasker , W. S. and Welsch , R. E. “The Use of Bounded-Influence Regression in Data Analysis: Theory, Computation, and Graphics,” . Proceedings: Computer Science and Statistics: Fourteenth Symposium on the Interface . pp. 45 – 51 . New York : Springer-Verlag. .
- Pregibon , D. 1981 . “Logistic Regression Diagnostics,” . Annals of Statistics , 9 : 705 – 724 .
- Welsch , R. E. 1980 . “Regression Sensitivity Analysis and Bounded Influence Estimation,”. ” . In Evaluation of Econometric-Models Edited by: Kmenta , J. and Ramsey , J. New York : Academic Press. .
- Welsch , R. E. and Kuh , E. 1977 . “Linear Regression Diagnostics,” Cambridge , MA : NBER Computer Research Center, Sloan School of Management, Massachusetts Institute of Technology. . Technical Report No. 173
- Welsch , R. E. and Peters , S. C. “Finding Influential Subsets of Data in Regression Models,” . Computer Science and Statistics: Eleventh Annual Symposium on the Interface . pp. 240 – 244 . North Carolina State University. .