On high breakdown point estimation

Computational Statistics

Volumn 11, Issue 2, 1996, Pages 137-146

  Víšek, Jan Á mos ^a,b  

^a INSTITUTE OF INFORMATION THEORY AND AUTOMATION   (Czech Republic)

^b CHARLES UNIVERSITY   (Czech Republic)

Author keywords

Diagnostics by high breakdown point; Diversity of estimates; Heuristics of high breakdown point; LMS and LTS new algorithms

Indexed keywords

EID: 0030556222     PISSN: 09434062     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (23)

1. Boček, P., Lachout, P. (1993), Linear programming approach to LMS-estimation, Memorial volume of Comput. Statist. & Data Analysis, 19 (1995), 129-134.
2. Boček, P., Víšek, J. Á. (1994), Significance of differences of estimates, Transactions of the Fifth Prague Symposium on Asymptotic Statistics, eds. M. Hušková & P. Mandl, Springer Verlag, 1994, 195-202.
3. Carroll, R. J., Ruppert, D. (1985), Transformations in regression: A robust analysis, Technometrics 27, 1-12.
4. Davis, P. L.(1993), Aspects of robust linear regression, Ann. Statist. 21, 1843-1899.
5. Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A. (1986), Robust Statistics - The Approach Based on Influence Functions, J. Wiley & Sons, New York.
6. Hettmansperger, T. P., Sheather, S. J. (1992), A Cautionary Note on the Method of Least Median Squares, The American Statistician 46, 79-83.
7. Joss, J., Marazzi, A. (1990), Probabilistic algorithms for LMS regression, Computational Statistics & Data Analysis 9, 123-134.
8. Jurečková, J., Portnoy, S. (1988), Asymptotics for one-step M-estimators in regression with application to combining efficiency and high breakdown point, Comunications in Statist. A 16, 2187-2199.
9. Kovanic, P. (1986), A new theoretical and algorithmical basis for estimation, identification and control, Automatica, 22, 657-674.
10. Martin, R.D., Yohai, V.J., Zamar, R.H. (1989), Min-max bias robust regression, Ann Statist. 17, 1608-1630.
11. Rousseeuw, P.J. (1984), Least median of square regression, Journal of Amer. Statist. Association 79, 871-880.
12. Rousseeuw, P.J. (1993), Unconventional features of positive-breakdown estimators, Report no. 93-26 of University of Antwerp, UIA, Belgium
13. Rousseeuw, P.J., Leroy, A.M. (1987), Robust Regression and Outlier Detection, J.Wiley & Sons, New York.
14. Rubio, A.M., Aguilar, L. Z., Víšek, J. Á. (1993), Testing for difference between estimates of model, Computational Statistics 8 (1993), 57-70.
15. Rubio, A.M., Quintana, F., Víšek, J. Á. (1994a), Sensitivity analysis of M-estimators of non-linear regression model, Comment. Math. Univ. Carolinae 35,1 (1994), 111-125.
16. Rubio, A.M., Quintana, F., Víšek, J. Á. (1994b), Diagnostics of regression model: Test of goodness of fit, Transactions of the Fifth Prague Symposium on Asymptotic Statistics, eds. M. Hušková & P. Mandl, Springer Verlag, 1994, 425-434.
17. Rubio, A.M., Quintana, F., Víšek, J. Á. (1994c), Diagnostics of non-linear regression. Transactions of the Twelfth Prague Conference on Information Theory, Statistical Decision Functions and Random Processes, Prague, 1994, 203-206.
18. Ruppert, D., Carroll, R. J. (1980), Trimmed least squares estimation in linear model, J. Americal Statist. Ass., 75 (372), 828-838.
19. Simpson, D. G., Ruppert, D., Carroll, R. J. (1992), On one-step G-M estimates and stability of inferences in linear regression, Journal of American Statistical Association, vol. 87, 439-450.
20. Víšek, J. Á. (1994a) A cautionary note on the method of Least Median of Squares reconsidered, Transactions of the Twelfth Prague Conference on Information Theory, Statistical Decision Functions and Random Processes, Prague, 1994, 254-259.
21. Víšek, J. Á (1994b), High robustness and an illusion of truth, Transactions of ROBUST'94, JČMF (Union of Czech mathematicians), eds. J. Antoch & G. Dohnal, ISBN 80-7015-492-6, 172-185.
22. Víšek, J.Á.(1994c), On the heuristics of statistical results, to appear in Proceedings of 'PROBASTAT'94'.
23. Víšek, J.Á.(1994d), Sensitivity analysis of M-estimates To appear in Annals of the Institute of Statistical Mathematics, Tokyo.