Linear M-estimation with bounded variables

BIT Numerical Mathematics

Volumn 37, Issue 1, 1997, Pages 13-23

  Edlund, Ove ^a  

^a LULEÅ UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

Bounded variables; Linear regression; M estimators; Robust regression

Indexed keywords

EID: 0010589655     PISSN: 00063835     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02510169     Document Type: Article

References (9)

1. O. Edlund, H. Ekblom, and K. Madsen, Algorithms for non-linear M-estimation, Research Report 1995-07, Department of Mathematics, Luleå University of Technology, Luleå, Sweden, 1995.
2. H. Ekblom and K. Madsen, Algorithms for non-linear Huber estimation, BIT, 29 (1989), pp. 60-76.
3. R. Fletcher, Practical Methods of Optimization, 2nd ed, John Wiley & Sons, 1987.
4. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimization, Academic Press, 1981.
5. M. D. Hebden, An algorithm for minimization using exact second derivatives, Report TP515, Atomic Energy Research Establishment, Harwell, England, 1973.
6. K. Madsen, A combined Gauss-Newton and Quasi-Newton method for non-linear least squares, Tech. Report NI-88-10, Institute for Numerical Analysis, Technical University of Denmark, Lyngby, Denmark, 1988.
7. K. Madsen and H. B. Nielsen, Finite algorithms for robust linear regression, BIT, 30 (1990), pp. 333-356.
8. J. J. Moré, The Levenberg-Marquardt algorithm: Implementation and theory, in Numerical Analysis, Proceedings Biennial Conference Dundee 1977, G. A. Watson, ed., Berlin, 1978, Springer-Verlag, pp. 105-116.
9. G. A. F. Seber and C. J. Wild, Nonlinear Regression, John Wiley & Sons, 1989.