Computing least trimmed squares regression with the forward search

Statistics and Computing

Volumn 9, Issue 4, 1999, Pages 251-263

  Atkinson, A C ^a   Cheng, T C ^a  

^a LONDON SCHOOL OF ECONOMICS   (United Kingdom)

Author keywords

Detection of outliers; Forward search algorithm; High breakdown point; Least trimmed squares; Regression diagnostics; Stalactite plot

Indexed keywords

EID: 0042046236     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1008942604045     Document Type: Article

References (22)

1. Atkinson, A. C. (1985) Plots, Transformations and Regression, Oxford: Oxford University Press.
2. Atkinson, A. C. (1994) Fast very robust methods for the detection of multiple outliers. Journal of the American Statistical Association, 89, 1329-1339.
3. Atkinson, A. C. and Mulira, H.-M. (1993) The stalactite plot for the detection of multivariate outliers. Statistics and Computing, 3, 27-35.
4. Coakley, C. W., Mili, L. and Cheniae, M. G. (1994) Effect of leverage on the finite sample efficiencies of high breakdown estimators. Statistics and Probability Letters, 19, 399-408.
5. Croux, C., Rousseeuw, P. J. and Hössjer, O. (1994) Generalized S-estimators. Journal of the American Statistical Association, 89, 1271-1281.
6. Davies, L. (1994) Desirable properties, breakdown and efficiency in the linear regression model. Statistics and Probability Letters, 19, 361-370.
7. Davies, P. L. (1993) Aspects of robust linear regression. The Annals of Statistics, 21, 1843-1899.
8. Hadi, A. S. and Luceño, A. (1997) Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms. Computational Statistics and Data Analysis, 25, 251-272.
9. Hawkins, D. M. (1994) The feasible solution algorithm for least trimmed squares regression. Computational Statistics and Data Analysis, 17, 185-196.
10. Hettmansperger, T. P. and McKean, J. W. (1998) Robust Non-parametric Statistical Methods, London: Arnold.
11. Hettmansperger, T. P. and Sheather, S. J. (1992) A cautionary note on the method of the least median squares. The American Statistician, 46, 79-83.
12. Hettmansperger, T. P. and Sheather, S. J. (1993) Reply to the comment of Rousseeuw, P. J., The American Statistician, 47, 162-163.
13. Hössjer, O. (1994) Rank-based estimates in the linear model With high breakdown point. Journal of the American Statistical Association, 89, 149-157.
14. Hössjer, O. (1995) Exact computation of the least trimmed squares estimate in simple linear regression. Computational Statistics and Data Analysis, 19, 265-282.
15. Morgenthaler, S. (1991) A note on efficient regression estimators with positive breakdown point. Statistics and Probability Letters, 11, 469-472.
16. Rousseeuw, P. J. (1984) Least median of squares regression. Journal of the American Statistical Association, 79, 871-880.
17. Rousseeuw, P. J. (1993) Comment on the paper of Hettmansperger, T. P. and Sheather, S. J., The American Statistician, 47, 162.
18. Rousseeuw, P. J. (1994) Unconventional features of positive-breakdown estimators. Statistics and Probability Letters, 19, 417-431.
19. Rousseeuw, P. J. and Leroy, A. M. (1987) Robust Regression and Outlier Detection, New York: John Wiley.
20. Stefanski, L. A. (1991) A note on high breakdown point estimators. Statistics and Probability Letters, 11, 353-358.
21. Yohai, V. J. (1987) High breakdown point and high efficiency robust estimators for regression. The Annals of Statistics, 15, 462-656.
22. Yohai, V. J. and Zamar, R. H. (1988) High breakdown point estimates of regression by means of the minimization of an efficient scale. Journal of the American Statistical Association, 83, 406-413.