Computing the constrained M-estimates for regression

Computational Statistics and Data Analysis

Volumn 49, Issue 1, 2005, Pages 19-32

  Edlund, Ove ^a   Ekblom, Håkan ^a  

^a LULEÅ UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

Algorithms; CM estimates; High breakdown point estimators for regression; Robust regression; Robustness; S estimates

Indexed keywords

COMPUTER SIMULATION; ESTIMATION; MATHEMATICAL MODELS; MONTE CARLO METHODS; PROBLEM SOLVING; REGRESSION ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS);

CM-ESTIMATES; POINT ESTIMATORS FOR REGRESSION; ROBUST REGRESSION; S-ESTIMATES;

COMPUTATIONAL METHODS;

EID: 13644261751     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2004.04.012     Document Type: Article

References (8)

1. Arslan O., Edlund O., Ekblom H. Algorithms to compute CM- and S-estimates for regression. Metrika. 55:2002;37-51
2. Edlund, O., 1999. Solution of linear programming and non-linear regression problems using linear M-estimation methods. Doctoral Thesis, Department of Mathematics, Luleå University of Technology, S-97187 Luleå, Sweden, URL:http://epubl.ltu.se/1402-1544/1999/17/LTU-DT-9917-SE. pdf
3. Edlund, O., 2004. CMregr - a Matlab software package for finding CM-estimates for regression, J. Statist. Software, URL:http://www.jstatsoft.org
4. Fletcher R. Practical Methods of Optimization. 2nd Edition:1987;Wiley, New York
5. Huber P. Robust Statistics. 1981;Wiley, New York
6. Mendes, B., Tyler, D.E., 1996. Constrained M-estimates for regression. In: H. Rieder (Ed.), Robust Statistics; Data Analysis and Computer Intensive Methods, Lecture Notes in Statistics, Vol. 109. Springer, New York, pp. 299-320.
7. Rousseeuw P.J., Yohai V.J. Robust regression by means of S-estimators. Frank J., Härdle W., Martin R.D. Robust and Nonlinear Time Series Analysis, Lecture Notes in Statistics. 1984;256-272 Springer, New York
8. Ruppert D. Computing S estimators for regression and multivariate location/dispersion. J. Comput. Graphical Statist. 1:1992;253-270